Course Schedule

Current semester Spring 2024, and future semesters are now available to view.

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• CSCI-GA.1170-​001
  (8657) Fundamental Algorithms  Yevgeniy Dodis W 4:55-6:55PM CIWW 109
  

  Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​002
  (8658) Fundamental Algorithms Recitation  Eli Goldin R 5:55-6:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001
  

  Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​003
  (8659) Fundamental Algorithms Recitation  Peter Hall F 4:55-5:45PM Silv 208 Additional information: Recitation for Section 001
  

  Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​004
  (22544) Fundamental Algorithms  Anupam Gupta T 4:55-6:55PM CIWW 312
  

  Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​006
  (22547) Fundamental Algorithms Recitation  Sihyun Lee R 3:45-4:35PM CIWW 317 Additional information: Recitation for Section 004
  

  Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1180-​001
  (8662) Mathematical Techniques For CS Applications  Parijat Dube W 7:10-9:10PM CIWW 101
  

  An introduction to theory, computational techniques, and applications of linear algebra, probability and statistics. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, natural language processing, and computer graphics. The course teaches a specialized language for mathematical computation, such as Matlab, and discusses how the language can be used for computation and for graphical output. No prior knowledge of linear algebra, probability, or statistics is assumed.

• CSCI-GA.2110-​001
  (8663) Programming Languages  Cory Plock M 4:55-6:55PM CIWW 109
  

  Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2110-​002
  (8664) Programming Languages Recitation  Rishu Singh R 7:10-8:00PM Bobst LL150 Additional information: Recitation for section 001
  

  Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2110-​003
  (23206) Programming Languages Recitation  Jacob Salzberg R 7:10-8:00PM Tisch LC9 Additional information: Recitation for Section 001
  

  Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2130-​001
  (8666) Compiler Construction  Joseph Tassarotti M 7:10-9:10PM CIWW 312
  This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation. The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory (formal languages), programming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum. This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator.
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
• CSCI-GA.2250-​001
  (8667) Operating Systems  Hubertus Franke M 7:10-9:10PM CIWW 109
  The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.
• CSCI-GA.2250-​002
  (8668) Operating Systems  Yang Tang T 4:55-6:55PM CIWW 101
  The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.
• CSCI-GA.2262-​001
  (8669) Data Communications and Networks  Jean-Claude Franchitti M 7:10-9:10PM CIWW 101
  

  This course teaches the design and implementation techniques essential for engineering robust networks. Topics include networking principles, Transmission Control Protocol/Internet Protocol, naming and addressing (Domain Name System), data encoding/decoding techniques, link layer protocols, routing protocols, transport layer services, congestion control, quality of service, network services, programmable routers and overlay networks.

  
  
  Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
• CSCI-GA.2274-​001
  (8670) Advanced Computer Graphics  Luiz Velho M 2:00-4:00PM CIWW 317
  

  This is a capstone course based on computer graphics tools. The course covers a selection of topics that may include computer animation, gaming, geometric modeling, motion capture, computational photography, physically based simulation, scientific visualization, and user interfaces. Not all areas are available every semester; the choice of areas is determined by the instructor. The capstone project involves some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, CSCI-GA 2250 and CSCI-GA 2270
• CSCI-GA.2421-​001
  MATH-GA 2020-001
  (19088) Numerical Methods II  Jonathan Goodman T 5:10-7:00PM CIWW 1302
  Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.
  
  Prerequisites: Corequisite: linear algebra.
• CSCI-GA.2433-​001
  DS-GA.2433-001
  (8671) Database Systems  Matthew Zeidenberg W 4:55-6:55PM Meyr 122
  Database system architecture. Modeling an application and logical database design. The relational model and relational data definition and data manipulation languages. Design of relational databases and normalization theory. Physical database design. Concurrency and recovery. Query processing and optimization.
• CSCI-GA.2436-​001
  (8718) Realtime and Big Data Analytics  Yang Tang R 4:55-6:55PM CIWW 101
  

  This course introduces architectures and technologies at the foundation of the Big Data movement. These technologies facilitate scalable management and processing of vast quantities of data collected through realtime and near realtime sensing. We explore tools enabling the acquisition of data in the social domain and the fusion of those data when in flight and at rest using Hadoop and Hadoop-related tools.

  The material covered in this course aligns with the prevailing state of the art in Big Data technologies, which continues to be a rapidly evolving landscape as new technologies emerge and existing ones evolve and mature.

  Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by designing, developing, and demonstrating an analytics project of their choosing. Class time will be set aside for project proposal and final demo.

  This is an introductory Hadoop course intended for students who have no experience with the Apache Hadoop ecosystem of tools and frameworks.

• CSCI-GA.2436-​002
  (23724) Realtime and Big Data Analytics  Lakshminarayanan Subramanian T 10:15-12:15PM 60 Fifth Ave C15
  

  This course introduces architectures and technologies at the foundation of the Big Data movement. These technologies facilitate scalable management and processing of vast quantities of data collected through realtime and near realtime sensing. We explore tools enabling the acquisition of data in the social domain and the fusion of those data when in flight and at rest using Hadoop and Hadoop-related tools.

  The material covered in this course aligns with the prevailing state of the art in Big Data technologies, which continues to be a rapidly evolving landscape as new technologies emerge and existing ones evolve and mature.

  Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by designing, developing, and demonstrating an analytics project of their choosing. Class time will be set aside for project proposal and final demo.

  This is an introductory Hadoop course intended for students who have no experience with the Apache Hadoop ecosystem of tools and frameworks.

• CSCI-GA.2437-​001
  (8719) Big Data Application Development  Yang Tang T 7:10-9:10PM CIWW 101
  

  This is an introductory course on Spark programming, Spark architecture, Spark SQL, Spark Streaming, and integration of Spark with the Hadoop ecosystem for developing Big Data analytics applications. The course project can be completed with Scala or Python, and Spark. This course covers technologies that integrate well with Spark in the creation of Big Data analytics applications.

  Students are required to complete weekly reading and programming assignments and demonstrate mastery of course topics by developing a final project using Spark.

  
  
  Prerequisites: Prerequisites include experience with Hadoop, which commonly comes from having completed the Realtime and Big Data Analytics course. There will be a review of Apache Hadoop, but students should have experience with Hadoop MapReduce and HDFS.
• CSCI-GA.2560-​001
  (8672) Artificial Intelligence   Paul Bethe M 4:55-6:55PM CIWW 101
  

  There are many cognitive tasks that people do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course covers problem solving and state space search; automated reasoning; probabilistic reasoning; planning; and knowledge representation.

• CSCI-GA.2565-​001
  (8673) Machine Learning   Saining Xie T 4:55-6:55PM Silv 208
  

  This course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course covers the mathematical methods and theoretical aspects but primarily focuses on algorithmic and practical issues.

  
  
  Prerequisites: Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics.
• CSCI-GA.2572-​001
  DS-GA.1008-001
  (8735) Deep Learning  Mengye Ren R 4:55-6:35PM 19UP 102
  

  This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

  
  
  Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
• CSCI-GA.2572-​002
  DS-GA.1008-002
  (8736) Deep Learning Lab  Alfredo Canziani T 5:55-6:45PM 19UP 102
  

  This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

  
  
  Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
• CSCI-GA.2621-​001
  (8716) Distributed Systems  Aurojit Panda W 4:55-6:55PM CIWW 101
  

  Large-scale distributed systems lie at the core of application domains such as cloud computing, internet of things, large multiplayer games, etc. These application domains make use of systems such as distributed databases (e.g., Google's Spanner, Amazon's Dynamo, etc.), large scale analytics frameworks (e.g., Hadoop, Spark and TensorFlow), distributed locking systems (e.g., Paxos, etcd, etc.), and others. In this class we will look at how to construct these distributed systems, in particular looking at why this is more complex than building applications running on a single machine, and present abstraction and design techniques for building distributed systems. We will focus on a solving a variety of common problems in these systems including consensus, consistency, naming, fault tolerance, etc.

  This is a graduate-level course, but undergraduates are welcome! The course itself will consist of a series of lectures and will require reading research papers.

  This class satisfies the Ph.D. breadth requirement in Systems and also serves as a M.S. capstone course.

  
  
  Prerequisites: 1. Undergraduate Operating Systems or Computer Systems Organization; 2. programming experience; 3. mathematical maturity or the willingness to work through simple proofs.
• CSCI-GA.2820-​001
  (8706) DevOps and Agile Methodologies  John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program
  

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.2820-​002
  (8707) DevOps and Agile Methodologies Lab  John Rofrano T 7:10-8:00PM Online Additional information: Open only to students in the Stern TECH MBA program
  

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.2820-​003
  (8708) DevOps and Agile Methodologies  John Rofrano W 4:55-6:55PM Online
  

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.2820-​004
  (809) DevOps and Agile Methodologies Lab  John Rofrano W 7:10-8:00PM Online
  

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.2830-​001
  (8710) The Lean Launch Pad*  Yiannis Broustas R 10:15-12:15PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

  Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

  This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands­ on learning on what it’s like to actually start a high­ tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.

• CSCI-GA.2830-​002
  (8711) The Lean Launch Pad Lab*  Yiannis Broustas R 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

  Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

  This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands­ on learning on what it’s like to actually start a high­ tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.

• CSCI-GA.2945-​002
  MATH-GA 2011-002
  (8674) Adv Topics Num Analysis: Convex & Non-Smooth Operation  Michael Overton W 5:10-7:00PM CIWW 1302
  

  This class will be an introduction to the fundamentals of parallel scientific computing. We will establish a basic understanding of modern computer architectures (CPUs and accelerators, memory hierarchies, interconnects) and of parallel approaches to programming these machines (distributed vs. shared memory parallelism: MPI, OpenMP, OpenCL/CUDA). Issues such as load balancing, communication, and synchronization will be covered and illustrated in the context of parallel numerical algorithms. Since a prerequisite for good parallel performance is good serial performance, this aspect will also be addressed. Along the way you will be exposed to important tools for high performance computing such as debuggers, schedulers, visualization, and version control systems. This will be a hands-on class, with several parallel (and serial) computing assignments, in which you will explore material by yourself and try things out. There will be a larger final project at the end. You will learn some Unix in this course, if you don't know it already.

  
  
  Prerequisites: Prerequisites for the course are (serial) programming experience with C/C++ (I will use C in class) or FORTRAN, and some familiarity with numerical methods.
• CSCI-GA.3033-​026
  (8688) Special Topics: Cloud Computing  Jean-Claude Franchitti R 7:10-9:10PM CIWW 101
  

  This course is a capstone course that introduces the latest cloud multi-access computing platform services including support for Internet of Things (IoT), Artificial Intelligence (AI), Web3 decentralized platforms, and other technologies. These services may be leveraged to create industry 4.0 and 5.0 innovative Intelligent Autonomous Networked (IAN) business solutions and deploy them on hybrid multi-cloud platforms so they can operate at the network edge to minimize latency and power consumption. Students will learn about services provided on the top “Big Clouds”, namely Amazon AWS Cloud, Google Cloud, Microsoft Azure Cloud, IBM Cloud, Oracle Cloud, Salesforce, and others. Related services provided on the Cloud and covered in the course include computing and hosting services, storage services, networking services, container management services (e.g., K8s), and shallow/deep machine learning services. Once familiar with Cloud services, Students will learn how to combine them to support cognitive computing and enable the creation of IAN solutions that cater to the practical needs of industry 4.0/5.0 and society 5.0 users in novel and creative ways. As they create these IAN solutions, students will learn about immersive Augmented/Virtual Reality (AR/VR) User Experience and Interfaces (UX/UI), hybrid decentralized computing and DApps, multi-access computing and Internet of Things (IoT), Mobility/5G networking and Software Defined Network (SDN) infrastructures, advanced robotics, and various key aspects of digital security that are critical to ensure the safety of modern software solutions. Throughout the course, students will develop their own Intelligent Autonomous Networked (IAN) business solutions using Cloud-based shallow/deep machine learning and other services and capabilities as noted above. Sample platforms and related application components will be provided as applicable to speed up the development and deployment process.

• CSCI-GA.3033-​029
  (8689) Special Topics: Social Networks  CANCELLED -
  

  Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. The World Wide Web enables information flows among vast number of humans; Facebook, and LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc, BitCoins, Ethereum, etc. allow parties to make financial and contractual transactions . These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorumsensing, swarming, flocking, social grooming, gossip, etc., and have evolved with the dynamics of evolutionary games (particularly Signaling Games). Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. Economic markets also look much more like networks than anonymous marketplaces. Firms interact with the suppliers and customers in a Web-like supply chains. Systemic risk in financial markets often results from the counterparty risks created within this financial network. This course will introduce the tools for the study of networks, and mechanism designed to interact strategically: SmartContractss, Costly Signaling, Credible and Noncredible Threats, Model Checking and Verification, Safety and Liveness, Recommenders and Verifiers, Deception, Market mechanisms (pricing and auction), Privacy and Trust… Exampled Systems: Bit-coins, Zcash, Ripple, Stellar, Ethereum, Solidity, Rholang, DAO (Distributed Companies), Cyber Security (M-coins), Information Finance Markets (WealthFront, etc.), Market Microstructure, ICO/STO/ITO, GDPR, OpenLaw, OpenZeplin, ...

• CSCI-GA.3033-​034
  (8690) Special Topics: Multicore Processors: Architecture & Programming  Mohamed Zahran W 4:55-6:55PM Online
  

  This course serves as a capstone for the MSCS program. 

  In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn the architecture and the programming of GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.

• CSCI-GA.3033-​052
  (8691) Special Topics: Advanced Machine Learning  Mehryar Mohri T 4:55-6:55PM CIWW 317
  

  This course discusses advanced topics in theoretical machine learning, extending beyond the scope of foundational graduate courses. The primary goal is to introduce key concepts not covered in basic ML courses such as Foundations of Machine Learning, while also exploring cutting-edge learning problems that can serve as a springboard for research or the development of novel application-relevant techniques.

  
  A central focus of the course is a rigorous analysis of the rich field of online learning. In addition, the material encompasses a broad range of advanced topics in supervised learning, providing a comprehensive overview of the theoretical underpinnings of modern machine learning methods.

  Through a combination of lectures, discussions, and assignments, students will gain a deep understanding of the fundamental principles governing learning algorithms. The course will equip students with the necessary theoretical foundations to conduct cutting-edge research in theoretical machine learning, as well as to develop novel and effective machine learning solutions for real-world problems.

• CSCI-GA.3033-​059
  (8692) Special Topics: Big Data Science  Anasse Bari W 4:55-6:55PM CIWW 202
  

  Data Science has recently gained a substantial interest in a variety of industries including healthcare, finance, manufacturing and marketing. It is positioned at the intersection point of computer science, data mining, machine learning, predictive analytics, business, statistics and domain expertise. It aims at finding hidden knowledge (patterns, models, associations) for the purpose of making better decisions. This course will introduce the basic fundamental concepts of the algorithms and the tools of extracting valuable insights from data with the ability to formulating and solving problems with it. Topics of this course include, but are not limited to: Basics of Data Analytics Project Lifecycle, Developing an Analytics Roadmap, Data Preprocessing, Data Analytics Use cases, Data Cleaning Algorithms, Feature Selection Algorithms, Introduction to Hadoop in Theory and in Practice, Algorithms for Finding Similar Data Items, Dimensionality Reduction techniques, Clustering Algorithms, Classification Algorithms, Introduction to Sentiment Analysis in short text (e.g Tweets), Mining Association Rules, Performance Measures, Ensemble Methods, Information retrieval systems and Recommendation Systems with applications in Bio-informatics, Text Mining and Social Network Analytics.  

• CSCI-GA.3033-​071
  (8693) Special Topics: Geometric Modeling  Daniele Panozzo R 4:55-6:55PM Silv 411
  

  Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangular meshes, surface reconstruction, mesh improvement, mesh parametrization, discrete differential geometry, interactive shape editing, skinning animation, architectural and structure-aware geometric modeling, and shape modeling with an eye on 3D printing. The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.

  
  
  Prerequisites: Experience with python programming, CSCI-UA 140 (or an equivalent undergraduate course in linear algebra). Some background in computer graphics is helpful, but not necessary.
• CSCI-GA.3033-​076
  (8694) Special Topics: Vision Meets Machine Learning  Davi Geiger W 7:10-9:10PM Bobst LL150
  

  This course will introduce basic ideas of current Machine Learning methods (Deep Learning with Variational Auto Encoders, GAN, RNN, Reinforcement Learning). The course is ""hands on"", with small groups programming/developing projects that integrate the basic ideas above to perform ""computer vision tasks"" (the evaluation is based on projects). The vision tasks will be in Object Classification, Human Detection, and Human Actions.

  
  
  Prerequisites: Students who take this class should have familiarity with linear algebra, probability, and calculus as well as programming in python.
• CSCI-GA.3033-​077
  (21238) Special Topics: Big Data and ML Systems  Jinyang Li W 10:15-12:15PM 60 Fifth Ave C15
  

  This class will discuss recent research on machine learning systems, esp. those targeted at accelerating deep learning workloads. We will take a deep dive exploring how these systems work so that ML models can be written in a high-level language and executed as low-level kernels on parallel hardware accelerators. Topics covered in this course include: basics of neural networks, how they are programmed and executed by today's deep learning frameworks, automatic differentiation, deep learning accelerators, distributed training techniques, computation graph optimizations, automated kernel generation.

   The class will require reading research papers, presenting papers, working on programming labs, and completing a significant final project.

  This is a capstone course for the MSCS program.

• CSCI-GA.3033-​079
  DS-GA.3001-013
  (8722) Special Topics: Mathematics of Deep Learning  Joan Bruna W 2:00-3:40PM 194M 204
  

  In this graduate-level course, we will explore selected mathematical aspects of neural networks, addressing their ability to extract useful information in high-dimensions when trained with gradient-descent methods, and with an emphasis on well-formulated open questions.  Requirements: solid foundations in linear algebra, analysis, probability and statistics. Notions of measure concentration, high-dimensional probability, statistical mechanics and harmonic analysis will be helpful, but not strictly required. 

  - Part I: Feature learning in neural networks: linear and non-linear approximation with shallow neural networks. single- and multi-index models. Quantitative guarantees of (S)GD. Computational lower bounds. Connections to related high-dimensional inference problems. Open problems. 

  - Part II: Generative modeling. Langevin Dynamics and Functional Inequalities. Beyond log-concavity. Score-based diffusion, stochastic interpolants and probability flows. Connections with Stochastic Control and Renormalization Groups. Quantitative guarantees using Neural Network Ansatze. Open problems. 

• CSCI-GA.3033-​085
  (8695) Special Topics: Cloud and Machine Learning  I-Hsin Chung
  Hao Yu R 4:55-6:55PM CIWW 202
  

  This course exposes students to the practical aspects of the state of art in cloud computing with an emphasis on employing cloud for machine learning problems. The course material introduces students to various cloud technologies such as virtualization and container. Students will learn how to build machine learning systems using cloud computing, the application performance characteristics, and develop hands-on experience with programming machine learning applications on the cloud platforms.

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
• CSCI-GA.3033-​090
  (8700) Special Topics: Deep Reinforcement Learning  Lerrel Pinto F 4:55-6:55PM CIWW 312
  

  Deep Reinforcement Learning (RL) has made massive strides in the last decade for sequential decision making problems. This course serves as a graduate-level introduction to RL, with an emphasis on applications and recent research. Key topics will include: Basic formalisms of RL; Exploration vs exploitation; Imitation learning; Model-free RL; Model-based control and planning; Unsupervised learning for RL; Applications to games, robotics, industry; Current frontiers. This course will involve several coding home-works where you will implement various algorithms, and a final project. Alternative titles for this course: Adaptive control and learning, Dynamic optimization.

  
  
  Prerequisites: Graduate level knowledge of Machine Learning, Computer Vision, and Deep Learning. Proficiency in Python to complete the home-works.
• CSCI-GA.3033-​099
  DS-GA 3001-009
  (8696) Special Topics in DS: Introduction to Computer Vision  Jean Ponce M 4:55-6:35PM 60 Fifth Ave 110
  

  This course aims to cover broad topics in computer vision, and is not primarily a deep learning course. We will cover topics in traditional computer vision such as camera geometry, image formation, segmentation, object recognition, classification, and detection.

   
• CSCI-GA.3033-​105
  (8697) Special Topics: Computing & Sustainability  CANCELLED -
  

  This is a research oriented, seminar-based, paper-discussion based class on hot research topics at the intersection of computing and sustainability. Research topics discussed in the class include: climate change monitoring, climate change mitigation and adaptation, AI techniques for climate modeling, carbon footprint analytics and reduction, green computing and green data centers, green buildings, smart grids and energy management, life cycle assessment, environmental monitoring and modeling, smart cities, circular economy and technologies for sustainable agriculture, transportation and water management.

• CSCI-GA.3033-​106
  (8698) Special Topics: Geometric Methods in Algorithm Design  Subhash Khot T 10:15-12:15PM CIWW 202
  

  For many NP-hard problems, one can efficiently compute approximate solutions with a non-trivial guarantee on the approximation ratio. Starting with the seminal work of Goemans and Williamson in early 90s, several approximation algorithms have been designed based on a semi-definite programming relaxation followed by a rounding scheme. This course  will cover many of these SDP-based algorithms along with other applications of SDPs in computer science. The course will also cover hardness of approximation results, showing that for several NP-hard problems, it remains NP-hard to compute approximations beyond a certain threshold.

• CSCI-GA.3033-​107
  (8699) Special Topics: Cryptography of Blockchains  Benedikt Bünz M 10:15-12:15PM 60 Fifth Ave C15
  

  Blockchains are distributed systems that are secured using consensus protocols, game theoretic incentives and cryptographic primitives. This course is a deep dive into the different cryptography that is used in blockchains. We will dive into different basic and advanced cryptographic primitives such as signatures, accumulators and merkle trees, threshold cryptography, zero-knowledge proofs, and others and explore how they are applied in the blockchain field.

  
  
  Prerequisites: Strong mathematical background, Introductory cryptography (CSCI-GA.3210-001) or similar. No knowledge of blockchains is required. The course is mostly self-contained. Contact instructor if unsure.
• CSCI-GA.3033-​108
  DS-GA.3001-003
  (8723) Special Topics: Causal Inference in Machine Learning  Kyunghyun Cho M 4:55-6:35PM 60 Fifth Ave 150
  

  Recent advances and successes in machine learning have had tremendous impacts on society. Despite such impact and successes, the currently dominant paradigm of machine learning, broadly represented by deep learning, relies almost entirely on capturing statistical correlations among features from data. As the famous phrase “correlation does not imply causation” suggests, such correlation-driven approaches cannot uncover causal relationships among variables of interest. In this introductory course, we aim to provide early-year M.Sc. and Ph.D students, who are already familiar with machine learning, with the foundational ideas and techniques in causal inference so that they can expand their knowledge and expertise beyond correlation-driven machine learning.
  
  Recommended courses: DS-GA 1005 Inference and Representation, DS-GA 1008 Deep Learning

  
  
  Prerequisites: DS-GA 1001 Introduction to Data Science (or an equivalent course); DS-GA 1002 Probability and Statistics for Data Science (or an equivalent course); DS-GA 1003 Machine Learning (or an equivalent course)
• CSCI-GA.3033-​109
  (20042) Special Topics: Computer Vision for Science and Engineering  David Fouhey T 4:55-6:55PM CIWW 202
  

  One of the most important emerging areas for computer vision and machine learning is in helping the sciences and engineering extract more and better information from their data. This course will explore the applications of computer vision in the sciences and engineering, including some of the challenges that are not normally seen in "standard" machine learning and vision. Classes will be a mix of lectures, discussion of recent work by students, and hands-on group projects at the intersection of ML and science.

  
  
  Prerequisites: Students should come with familiarity with computer vision or machine learning as well as an enthusiasm for trying new data but no formal prerequisites are required.
• CSCI-GA.3033-​110
  (22514) Special Topics: Technologies for Finance  Anirudh Sivaraman T 7:10-9:10PM CIWW 102
  

  This is a new masters seminar that will explore topics at the intersection of finance and computing technologies. The course will include a mix of lectures from the instructor, lectures from invited speakers, and assigned readings.
  
  Tentative list of topics:
  Network technologies: microwave links, FPGAs, layer 1 switches, multicast, programmable switches, Cloud technologies: Challenges of operating exchange infrastructure in the cloud, Synchronized clocks: The role of synchronized time in stock exchanges: PTP, WhiteRabbit, PPS, Software solutions, etc.; High-frequency trading: History, trading algorithms, limit order books, market making, speed races, batch auctions, AI meets finance: BloombergGPT and related technologies, Secure payments, Programming language techniques: OCaml, Confidential computing for fintech, Central bank digital currencies, Distributed systems: Consensus, designing central exchange servers, etc., Dark pools

• CSCI-GA.3033-​111
  (22516) Special Topics: Conceptual Gaps in Modern Machine Learning  Matus Telgarsky T 2:00-4:00PM 60 Fifth Ave C15
  

  This class will survey various areas of modern machine learning, targeting conceptual gaps --- areas where the methods seem to work, but no one can explain why. Topics include but are not limited to: transformers (training challenges, and generalization performance), large language models (RLHF, auto-regressive training, in-contex learning), diffusion models (sampling and training), deep networks (optimization, generalization, architecture selection, all with an eye to feature learning), and distribution shift. Time permitting, there will be discussion of less popular topics and open problems from previous generations (e.g., characterizing the path followed by first-order methods). Grades will be based primarily on projects, and to a lesser extent upon paper reading and associated discussions.

  Projects will be open-ended, aiming to shed light on various gaps identified throughout the semester, and students may take any combination of applied and theoretical approaches in their investigations.

• CSCI-GA.3033-​934
  (8701) Special Topics: Multicore Processors: Architecture & Programming Recitation  Amitkumar Patel F 12:30-1:20PM Silv 401 Additional information: Recitation for section 034
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​979
  (8725) Special Topics: Mathematics of Deep Learning Recitation  STAFF F 11:15-12:05PM GCASL 383 Additional information: Recitation for section 079
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​999
  (8702) Special Topics: Introduction to Computer Vision Recitation  Simran Makariye R 5:55-6:45PM 60 Fifth Ave C15 Additional information: Recitation for section 099
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​9108
  DS-GA.3001-004
  (19825) Special Topics: Causal Inference in Machine Learning Recitation  STAFF T 4:55-5:45PM Silv 520 Additional information: Recitation for section 0108
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3140-​001
  (8717) Abstract Interpretation  Patrick Cousot M 4:55-6:55PM CIWW 202
  

  Many numerical or symbolic problems are very hard or even undecidable meaning that no computer algorithm can solve them exactly in reasonable time and memory space. This course introduces the latest techniques in abstract interpretation, a powerful framework for automatically calculating approximate solutions of such difficult problems. The main emphasis will be on software verification and automatic analysis with a wide range of applications from compilers to social networks.

  
  
  Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected.
• CSCI-GA.3205-​001
  (8676) Applied Cryptography & Network Security  Mazdak Zamani T 4:55-6:55PM Silv 206
  This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
• CSCI-GA.3812-​001
  (8703) Information Technology Projects *  Evan Korth W 5:00-8:00PM CIWW 317
  

  This is a capstone course that connects students directly with real-world information technology problems. The goal of this course is to teach the skills needed for success in real-world information technology via a combination of classroom lectures and practical experience with large projects that have been specified by local “clients.” The typical clients are primarily companies, but can also be government agencies or nonprofit organizations. Each project lasts for the entire semester and is designed to involve the full software project life cycle. Examples of such projects are development of software to solve a business problem, including specifying requirements, writing and testing prototype code, and writing a final report; and evaluation of commercial software to be purchased to address a business problem, including gathering requirements, designing an architecture to connect the new software with existing systems, and assessing the suitability of available software products.

  
  
  Prerequisites: For MS in IS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms and two of the following three courses: CSCI-GA 2262 Data Communications & Networks; CSCI-GA 2250 Operating Systems; CSCI-GA 2433 Database Systems. For MS in CS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms, CSCI-GA 2110 Programming Languages, and CSCI-GA 2250 Operating Systems. PERMISSION OF THE DEPARTMENT REQUIRED.
• CSCI-GA.3813-​001
  (8677) Adv Lab MS *  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​002
  (8678) Adv Lab MS *  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​003
  (8679) Adv Lab MS *  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​004
  (8680) Adv Lab MS *  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​005
  (8681) Adv Lab PhD **  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​006
  (8682) Adv Lab PhD **  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​007
  (8683) Adv Lab PhD **  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3813-​008
  (8684) Adv Lab PhD **  STAFF -
  

  Large-scale programming project or research in cooperation with a faculty member.

  
  
  Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
• CSCI-GA.3840-​001
  (8685) Master's Thesis Research *  STAFF -
  
  
  Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
• CSCI-GA.3850-​001
  (20699) PhD Seminar: Cryptography **  Yevgeniy Dodis W 1:00-3:30PM CIWW 412
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-GA.3850-​004
  (20707) PhD Seminar: Formal Methods  Joseph Tassarotti R 11:00-12:30PM 60 Fifth Ave 446
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-GA.3860-​001
  (20695) PhD Thesis Research **  STAFF -
  
  
  Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
• CSCI-GA.3870-​001
  (8686) Internship In Computer Science MS*  STAFF -
  

  Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

  Section 1 is for Master's students. Section 2 is for Ph.D. students.

  
  
  Prerequisites: Permission of Director of Graduate Studies.
• CSCI-GA.3870-​002
  (8687) Internship in Computer Science PhD*  STAFF -
  

  Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

  Section 1 is for Master's students. Section 2 is for Ph.D. students.

  
  
  Prerequisites: Permission of Director of Graduate Studies.
• CSCI-UA.0002-​001
  (7939) Intro To Computer Programming (No Prior Experience)  Amanda Steigman MW 8:00-9:15AM CIWW 202
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​002
  (7940) Intro To Computer Programming (No Prior Experience)  Julie Lizardo TR 8:00-9:15AM Online
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​003
  (7941) Intro To Computer Programming (No Prior Experience)  Craig Kapp MW 9:30-10:45AM CIWW 109
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​004
  (7942) Intro To Computer Programming (No Prior Experience)  Nathan Miranda TR 9:30-10:45AM CIWW 317
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​005
  (7943) Intro To Computer Programming (No Prior Experience)  Waqar Aqeel MW 11:00-12:15PM CIWW 202
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​006
  (7944) Intro To Computer Programming (No Prior Experience)  Joshua Clayton TR 11:00-12:15PM GCASL 388
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​007
  (7945) Intro To Computer Programming (No Prior Experience)  David Prager MW 12:30-1:45PM GCASL 383
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​008
  (7946) Intro To Computer Programming (No Prior Experience)  Caitlin Rubin TR 12:30-1:45PM GCASL 388
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​009
  (7947) Intro To Computer Programming (No Prior Experience)  Karl Rosenberg TR 2:00-3:15PM 60 Fifth Ave 110
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​010
  (7948) Intro To Computer Programming (No Prior Experience)  Emily Zhao MW 3:30-4:45PM Meyr 122
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​011
  (7949) Intro To Computer Programming (No Prior Experience)  Khye Borg Liew TR 3:30-4:45PM Online
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​012
  (7950) Intro To Computer Programming (No Prior Experience)  David Prager TR 8:00-9:15AM 12WV L120
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​013
  (7951) Intro To Computer Programming (No Prior Experience)  CANCELLED MW 8:00-9:15AM CIWW 312
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​014
  (7952) Intro To Computer Programming (No Prior Experience)  Mihir Patil MW 9:30-10:45AM CIWW 202
  

  An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0003-​001
  (8034) Intro to Computer Programming (Limited Prior Experience)  Adam Meyers TR 11:00-12:15PM Silv 408
  

  Introduces object-oriented programming, recursion, and other important concepts to students who already have had some exposure to programming in the context of building applications using Python. Students will design and implement Python programs in a variety of applied areas.

  
  
  Prerequisites: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with some previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0003-​002
  (8035) Intro to Computer Programming (Limited Prior Experience)  CANCELLED MW 9:30-10:45AM CIWW 312
  

  Introduces object-oriented programming, recursion, and other important concepts to students who already have had some exposure to programming in the context of building applications using Python. Students will design and implement Python programs in a variety of applied areas.

  
  
  Prerequisites: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with some previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0004-​001
  (7953) Intro to Web Design & Computer Principles  Alvaro Olsen MW 8:00-9:15AM CIWW 101
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​002
  (7954) Intro to Web Design & Computer Principles  Adam Scher TR 8:00-9:15AM Online
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​003
  (7955) Intro to Web Design & Computer Principles  Mark Ramos MW 11:00-12:15PM GCASL 279
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​004
  (7956) Intro to Web Design & Computer Principles  Joshua Clayton TR 2:00-3:15PM Silv 509
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​005
  (7957) Intro to Web Design & Computer Principles  Emily Zhao MW 12:30-1:45PM Bobst LL138
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​006
  (7958) Intro to Web Design & Computer Principles  Alvaro Olsen MW 3:30-4:45PM GCASL 279
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0060-​001
  (7959) Database Design And Implementation  Amos Bloomberg TR 11:00-12:15PM Online
  

  Introduces principles and applications of database design and working with data. Students use Python as they prepare, analyze, and work with data and use SQL to study the principles and implementations of relational databases. Also covers other database paradigms, such as NoSQL. Students apply these principles to computer systems and their respective fields of interest.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Students that have successfully completed Data Management and Analysis (CSCI-UA 479) are NOT eligible to take this course.
• CSCI-UA.0060-​002
  (7960) Database Design And Implementation  David Prager MW 11:00-12:15PM GCASL 383
  

  Introduces principles and applications of database design and working with data. Students use Python as they prepare, analyze, and work with data and use SQL to study the principles and implementations of relational databases. Also covers other database paradigms, such as NoSQL. Students apply these principles to computer systems and their respective fields of interest.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Students that have successfully completed Data Management and Analysis (CSCI-UA 479) are NOT eligible to take this course.
• CSCI-UA.0061-​001
  (7961) Web Development And Programming  Craig Kapp MW 3:30-4:45PM Bobst LL138
  

  Provides a practical approach to web technologies and programming. Students build interactive, secure, and powerful web programs. Covers client and server side technologies for the web.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), and Introduction to Web Design and Computer Principles (CSCI-UA 4). Students that have successfully completed Applied Internet Technology (CSCI-UA 467) are NOT eligible to take this course.
• CSCI-UA.0061-​002
  (7962) Web Development And Programming  Emily Zhao TR 3:30-4:45PM GCASL 369
  

  Provides a practical approach to web technologies and programming. Students build interactive, secure, and powerful web programs. Covers client and server side technologies for the web.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), and Introduction to Web Design and Computer Principles (CSCI-UA 4). Students that have successfully completed Applied Internet Technology (CSCI-UA 467) are NOT eligible to take this course.
• CSCI-UA.0101-​001
  (7963) Intro To Computer Science  Nader Nassar MW 8:00-9:15AM Silv 401
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​002
  (7964) Intro To Computer Science  Gabriel Provencher Langlois TR 8:00-9:15AM Silv 414
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​003
  (7965) Intro To Computer Science  Gezim Hakramaj MW 11:00-12:15PM Silv 411
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​004
  (7966) Intro To Computer Science  Tobias Blickhan TR 11:00-12:15PM CIWW 312
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​005
  (7967) Intro To Computer Science  Hilbert Gene Locklear MW 12:30-1:45PM GCASL 288
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​006
  (7968) Intro To Computer Science  Candido Cabo TR 12:30-1:45PM GCASL 269
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​007
  (7969) Intro To Computer Science  Hilbert Gene Locklear MW 2:00-3:15PM CIWW 101
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​008
  (7970) Intro To Computer Science  Anasse Bari TR 2:00-3:15PM Meyr 122
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​009
  (7971) Intro To Computer Science  Michael Tao MW 3:30-4:45PM CIWW 101
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​010
  (7972) Intro To Computer Science  Daniel Zint TR 3:30-4:45PM CIWW 101
  

  How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0102-​001
  (7973) Data Structures  Joanna Klukowska MW 8:00-9:15AM Cant 200
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​002
  (7974) Data Structures - Recitation  Anirudh Nistala F 8:00-9:15AM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​003
  (7975) Data Structures  Anasse Bari TR 11:00-12:15PM Bobst LL138
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​004
  (7976) Data Structures - Recitation  Norihito Naka F 9:30-10:45AM CIWW 202 Additional information: In-person recitation for Section 3
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​005
  (7977) Data Structures  Evan Korth TR 12:30-1:45PM Silv 408
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​006
  (7978) Data Structures - Recitation  Arunima Mitra F 8:00-9:15AM CIWW 317 Additional information: In-person recitation for Section 5
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​007
  (7979) Data Structures  Sanghyun Woo MW 11:00-12:15PM CIWW 317
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​008
  (7980) Data Structures - Recitation  Adithya Jairam Vedagiri Iyer F 9:30-10:45AM Bobst LL138 Additional information: In-person recitation for Section 7
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​009
  (7981) Data Structures  Sungmin Cha TR 2:00-3:15PM CIWW 317
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​010
  (7982) Data Structures - Recitation  Nishtha Agrawal F 2:00-3:15PM Tisch LC11 Additional information: In-person recitation for Section 9
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​011
  (7983) Data Structures  Alan Amin MW 12:30-1:45PM CIWW 317
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​012
  (7984) Data Structures - Recitation  Charlie Chen F 12:30-1:45PM CIWW 317 Additional information: In-person recitation for Section 11
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​021
  (7985) Data Structures - Recitation  Daniel Jin F 9:30-10:45AM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​022
  (7986) Data Structures - Recitation  Anirudh Nistala F 11:00-12:15PM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​023
  (7987) Data Structures - Recitation  Joel Tellis F 12:30-1:45PM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​024
  (7988) Data Structures - Recitation  Daniel Jin F 2:00-3:15PM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​025
  (7989) Data Structures - Recitation  Joel Tellis F 3:30-4:45PM CIWW 312 Additional information: In-person recitation for Section 1
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​041
  (7990) Data Structures - Recitation  Tanya Pushkin Garg F 2:00-3:15PM CIWW 202 Additional information: In-person recitation for Section 3
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​061
  (7991) Data Structures - Recitation  Arunima Mitra F 9:30-10:45AM CIWW 317 Additional information: In-person recitation for Section 5
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​062
  (7992) Data Structures - Recitation  Max Sklar F 11:00-12:15PM CIWW 317 Additional information: In-person recitation for Section 5
  

  Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0201-​001
  (7993) Computer Systems Organization  Douglas Moody MW 9:30-10:45AM CIWW 101
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​002
  (7994) Computer Systems Organization - Recitation  Stacy Chao F 8:00-9:15AM Silv 414 Additional information: In-person recitation for Section 1
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​003
  (7995) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Silv 405
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​004
  (7996) Computer Systems Organization - Recitation  Arsalan Anwar F 8:00-9:15AM Silv 405 Additional information: In-person recitation for Section 3
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​005
  (7997) Computer Systems Organization  Benjamin Goldberg MW 3:30-4:45PM Silv 405
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​006
  (7998) Computer Systems Organization - Recitation  Keyur Panchal F 8:00-9:15AM Silv 408 Additional information: In-person recitation for Section 5
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​007
  (7999) Computer Systems Organization  Gizem Kayar TR 3:30-4:45PM Silv 206
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​008
  (8000) Computer Systems Organization - Recitation  Abhishek Narayanan F 8:00-9:15AM 60 Fifth Ave 150 Additional information: In-person recitation for Section 7
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0202-​001
  (8001) Operating Systems  Michael Walfish MW 3:30-4:45PM Cant 101
  

  Covers the principles and design of operating systems. Topics include process scheduling and synchronization, deadlocks, memory management (including virtual memory), input/output, and file systems. Programming assignments.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0202-​002
  (8002) Operating Systems  Mohamed Zahran TR 11:00-12:15PM Online
  

  Covers the principles and design of operating systems. Topics include process scheduling and synchronization, deadlocks, memory management (including virtual memory), input/output, and file systems. Programming assignments.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0310-​001
  (8003) Basic Algorithms  Chee Yap MW 8:00-9:15AM CIWW 109
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​002
  (8004) Basic Algorithms - Recitation  Bingwei Zhang F 9:30-10:45AM 194M 208 Additional information: In-person recitation for Section 1
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​003
  (8005) Basic Algorithms  Alan Siegel TR 2:00-3:15PM Online
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​004
  (8006) Basic Algorithms - Recitation  Alan Siegel F 9:30-10:45AM Online Additional information: Online recitation for Section 3
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​005
  (8007) Basic Algorithms  Jiaxin Guan MW 9:30-10:45AM CIWW 317
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​006
  (8008) Basic Algorithms - Recitation  Yuqi Zhang F 8:00-9:15AM CIWW 101 Additional information: In-person recitation for Section 5
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​007
  (8009) Basic Algorithms  Neelima Gupta MW 3:30-4:45PM CIWW 109
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​008
  (8010) Basic Algorithms - Recitation  Sparsh Sanchorawala F 8:00-9:15AM Silv 208 Additional information: In-person recitation for Section 7
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​021
  (8011) Basic Algorithms - Recitation  TBA F 9:30-10:45AM CIWW 101 Additional information: In-person recitation for Section 1
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​022
  (8012) Basic Algorithms - Recitation  Bingwei Zhang F 2:00-3:15PM 194M 206 Additional information: In-person recitation for Section 1
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0330-​001
  MATH-UA.0144
  (8028) Introduction to Computer Simulation  Charles Peskin TR 11:00-12:15PM 19W4 102
  

  In this course, students will learn how to do computer simulations of such phenomena as orbits (Kepler problem and N-body problem), epidemic and endemic disease (including evolution in response to the selective pressure of a malaria), musical stringed instruments (piano, guitar, and violin), and traffic flow in a city (with lights, breakdowns, and gridlock at corners). The simulations are based on mathematical models, numerical methods, and Matlab programming techniques that will be taught in class. The use of animations (and sound where appropriate) to present the results of simulations will be emphasized.

  
  
  Prerequisites: Calculus I (MATH-UA 121) or Math for Economics II (MATH-UA 212) and General Physics (PHYS-UA 11).
• CSCI-UA.0330-​002
  (8029) Introduction to Computer Simulation  TBA F 11:00-12:15PM GCASL 388
  

  In this course, students will learn how to do computer simulations of such phenomena as orbits (Kepler problem and N-body problem), epidemic and endemic disease (including evolution in response to the selective pressure of a malaria), musical stringed instruments (piano, guitar, and violin), and traffic flow in a city (with lights, breakdowns, and gridlock at corners). The simulations are based on mathematical models, numerical methods, and Matlab programming techniques that will be taught in class. The use of animations (and sound where appropriate) to present the results of simulations will be emphasized.

  
  
  Prerequisites: Calculus I (MATH-UA 121) or Math for Economics II (MATH-UA 212) and General Physics (PHYS-UA 11).
• CSCI-UA.0380-​001
  (8013) Topics of General Interest: Drawing on the Web  Joshua Clayton MW 3:30-4:45PM CIWW 312
  Detailed descriptions available when topics are announced. Typical offerings include Computing in the Humanities and Arts and Introduction to Flash Programming. Does not count toward the computer science major.
  
  Prerequisites: Topics determine prerequisites.
• CSCI-UA.0381-​001
  (8037) Programming Tools for the Data Scientist  Hasan Aljabbouli TR 3:30-4:45PM Silv 411
  

  Lecture and laboratory.  Building applications in Python using a project-based learning approach. Students use Python packages in a variety of applied areas, such as textual analysis and data visualization. Does not count toward the computer science major. 

  
  
  Prerequisites: Data Science for Everyone (DS-UA 111) or equivalent proficiency in Python, and either Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3).
• CSCI-UA.0421-​001
  (8014) Numerical Computing   Gizem Kayar TR 11:00-12:15PM CIWW 101
  

  The need for floating-point arithmetic, the IEEE floating-point standard, and the importance of numerical computing in a wide variety of scientific applications. Fundamental types of numerical algorithms: direct methods (e.g., for systems of linear equations), iterative methods (e.g., for a nonlinear equation), and discretization methods (e.g., for a differential equation). Numerical errors: can you trust your answers? Uses graphics and software packages such as Matlab. Programming assignments.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201), either Calculus I (MATH-UA 121) or both of Mathematics for Economics I and II (MATH-UA 211 and 212), and Linear Algebra (MATH-UA 140), or permission of instructor.
• CSCI-UA.0467-​001
  (8038) Applied Internet Technology  Joseph Versoza TR 9:30-10:45AM CIWW 101
  

  Applied Internet Technology is a practical introduction to creating modern web applications. It covers full-stack (that is, every aspect of building a database driven web application: server programming, database implementation, frontend markup, styling and interactivity) web development. It includes topics such as database and data model design, web application architecture, separation of logic and presentation, handling user input and processing form data, managing asynchronous processes, strategies for creating real-time web applications, and handling client-side interactivity. Students will use current server and client-side web frameworks and libraries to build dynamic, data-driven sites. Various applications to support development will also be introduced, such as version control, static analysis tools, and build systems.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201). Students who complete Applied Internet Technology (CSCI-UA 467) with a C or better are not eligible to take Web Development and Programming (CSCI-UA 61).
• CSCI-UA.0467-​002
  (8039) Applied Internet Technology  Joseph Versoza MW 12:30-1:45PM CIWW 109
  

  Applied Internet Technology is a practical introduction to creating modern web applications. It covers full-stack (that is, every aspect of building a database driven web application: server programming, database implementation, frontend markup, styling and interactivity) web development. It includes topics such as database and data model design, web application architecture, separation of logic and presentation, handling user input and processing form data, managing asynchronous processes, strategies for creating real-time web applications, and handling client-side interactivity. Students will use current server and client-side web frameworks and libraries to build dynamic, data-driven sites. Various applications to support development will also be introduced, such as version control, static analysis tools, and build systems.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201). Students who complete Applied Internet Technology (CSCI-UA 467) with a C or better are not eligible to take Web Development and Programming (CSCI-UA 61).
• CSCI-UA.0469-​001
  (22325) Natural Language Processing  Adam Meyers TR 2:00-3:15PM 5WP 101
  

  Natural Language Processing (aka Computational Linguistics) is an inter-disciplinary field applying methodology of computer science and linguistics to the processing of natural languages (English, Chinese, Spanish, Japanese, etc.). Typical applications include: information extraction (automatically finding information from text); information retrieval (web searches and other applications involving the automatic selection of "relevant" documents); sentiment analysis (automatic extraction of opinions about a set of issues); and machine translation (automatically translating one natural language to another). Much of the best work in the field combines two methodologies: (1) automatically acquiring statistical information from one set of "training" documents to use as the basis for probabilistically predicting the distribution of similar information in new documents; and (2) using manually encoded linguistic knowledge. For example, many supervised methods of machine learning require: a corpus of text with manually encoded linguistic knowledge, a set of procedures for acquiring statistical patterns from this data and a transducer for predicting these same distinctions in new text. This class will cover linguistic, statistical and computational aspects of this exciting field.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0470-​001
  (8015) Object Oriented Programming  Hasan Aljabbouli MW 9:30-10:45AM Silv 401
  

  Introduces the important concepts of object-oriented design and languages, including code reuse, data abstraction, inheritance, and dynamic overloading. Covers in depth those features of Java and C++ that support object-oriented programming and gives an overview of other object-oriented languages of interest. Significant programming assignments stressing object-oriented design. Not offered every academic year.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0472-​001
  (8016) Artificial Intelligence   Ernest Davis MW 11:00-12:15PM Online
  Many cognitive tasks that people can do easily and almost unconsciously have proven extremely difficult to program on a computer. Artificial intelligence tackles the problem of developing computer systems that can carry out these tasks. Focus is on three central areas in AI: representation and reasoning, machine learning, and natural language processing.
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310).
• CSCI-UA.0473-​001
  (8030) Fundamentals of Machine Learning  Pascal Wallisch MW 11:00-12:15PM CIWW 109
  

  This exciting and fast-evolving field of computer science has many recent consumer applications (e.g., Microsoft Kinect, Google Translate, IPhone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). Students learn the theoretical foundations and how to apply machine learning to solve new problems.

  
  
  Prerequisites: Data Structures (CSCI-UA.102), Linear Algebra (MATH-UA.140) and Probability and Statistics (MATH-UA.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0473-​002
  (8031) Fundamentals of Machine Learning - Recitation  Duygu Ataman F 9:30-10:45AM 60 Fifth Ave 150 Additional information: In-person recitation for Section 1
  

  This exciting and fast-evolving field of computer science has many recent consumer applications (e.g., Microsoft Kinect, Google Translate, IPhone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). Students learn the theoretical foundations and how to apply machine learning to solve new problems.

  
  
  Prerequisites: Data Structures (CSCI-UA.102), Linear Algebra (MATH-UA.140) and Probability and Statistics (MATH-UA.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0473-​021
  (8032) Fundamentals of Machine Learning - Recitation  Duygu Ataman F 2:00-3:15PM 60 Fifth Ave 150 Additional information: In-person recitation for Section 1
  

  This exciting and fast-evolving field of computer science has many recent consumer applications (e.g., Microsoft Kinect, Google Translate, IPhone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). Students learn the theoretical foundations and how to apply machine learning to solve new problems.

  
  
  Prerequisites: Data Structures (CSCI-UA.102), Linear Algebra (MATH-UA.140) and Probability and Statistics (MATH-UA.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0474-​001
  (8017) Software Engineering  Amos Bloomberg MW 2:00-3:15PM Online
  

  An intense hands-on study of practical techniques and methods of software engineering. Topics include advanced object-oriented design, design patterns, refactoring, code optimization, universal modeling language, threading, user interface design, enterprise application development, and development tools. All topics are integrated and applied during the semester long group project. The aim of the project is to prepare students for dynamics in a real workplace. Members of the group meet on a regular basis to discuss the project and to assign individual tasks. Students are judged primarily on the final project presentations. Not offered every academic year. 

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0476-​001
  (8036) Processing Big Data for Analytics Applications  Ann Malavet TR 8:00-9:15AM CIWW 101
  

  Introduces platforms, tools, and architectures that facilitate scalable management and processing of vast quantities of data. Explores open source tools enabling the efficient acquisition, distributed storage, and processing of big data. Provides hands-on experience with distributed processing Apache solutions such as Hadoop MapReduce, HBase, Hive, Impala, Pig, core Spark, Spark SQL, and Spark Streaming. Other Apache big data tools covered are Oozie, Zookeeper, Flume, and Kafka.

  
  
  Prerequisites: (CSCI-UA 201) Computer Systems Organization and (CSCI-UA 310) Basic Algorithms. Familiarity with Linux commands and SQL will be helpful but is not required.
• CSCI-UA.0478-​001
  MATH-UA.0243
  (8018) Introduction To Cryptography  Marshall Ball MW 9:30-10:45AM 7E12 LL23
  An introduction to the principles and practice of cryptography and its application to network security. Topics include symmetric-key encryption (block ciphers, modes of operations, AES), message authentication (pseudorandom functions, CBC-MAC), public-key encryption (RSA, ElGamal), digital signatures (RSA, Fiat-Shamir), and authentication applications (identification, zero-knowledge).
  
  Prerequisites: Basic Algorithms (CSCI-UA 310).
• CSCI-UA.0479-​001
  (8040) Data Management and Analysis  Joseph Versoza MW 11:00-12:15PM CIWW 101
  

  Extracting, transforming and analyzing data in myriad formats. Using traditional relational
  databases as well as non-relational databases as well as non-relational databases to store, manipulate, and query data. Students write custom programs, create queries, and use data analysis tools and libraries on a wide array of data sets.  Additional topics: data modeling, cloud databases and AP programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0479-​002
  (8041) Data Management and Analysis  Matthew Zeidenberg TR 11:00-12:15PM 60 Fifth Ave 150
  

  Extracting, transforming and analyzing data in myriad formats. Using traditional relational
  databases as well as non-relational databases as well as non-relational databases to store, manipulate, and query data. Students write custom programs, create queries, and use data analysis tools and libraries on a wide array of data sets.  Additional topics: data modeling, cloud databases and AP programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0479-​003
  (8042) Data Management and Analysis  Matthew Zeidenberg MW 3:30-4:45PM Silv 401
  

  Extracting, transforming and analyzing data in myriad formats. Using traditional relational
  databases as well as non-relational databases as well as non-relational databases to store, manipulate, and query data. Students write custom programs, create queries, and use data analysis tools and libraries on a wide array of data sets.  Additional topics: data modeling, cloud databases and AP programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0480-​041
  (8019) Special Topics: Computer Graphics  Gizem Kayar TR 12:30-1:45PM CIWW 101
  

  This course provides an introduction to the field of Computer Graphics. We will cover the basic mathematical concepts, such as 2D and 3D transformations, study the interaction of light with geometry to derive shading models, and implement rendering algorithms such as raytracing and rasterization. We will investigate how these fundamental components are integrated into current graphics processors and study the corresponding programming APIs.

• CSCI-UA.0480-​051
  (8020) Special Topics: Parallel Computing  Mohamed Zahran TR 2:00-3:15PM Online
  

  A microprocessor with a single core no longer exists in almost all computers we are using today (including your tablet, smartphone, and even smartwatch). Most of our devices are now multicore processors. A multicore processor contains several cores (called CPUs or cores) on-chip. There are also accelerator chips, like GPUs. To make the best use of these multicore chips, we need to program them in-parallel. Sequential programming, for all platforms from smartphones to supercomputers, is falling out of fashion and taking a back-seat to parallel programming. How to think in parallel? How to write code in parallel to make the best use of the underlying hardware? How is this hardware different from the traditional one? What will the future be for software and hardware? This is what this course is about.

• CSCI-UA.0480-​061
  (8022) Special Topics: Open Source Software Development  Joanna Klukowska MW 12:30-1:45PM 194M 305
  Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.
  
  Prerequisites: Topics determine prerequisites.
• CSCI-UA.0480-​062
  (8023) Special Topics: Computer Networks  Anirudh Sivaraman MW 12:30-1:45PM GCASL 388
  

  This course will introduce students to computer networks, using today's Internet as an example. Topics covered include socket programming, routing, forwarding, reliable transmission, congestion control, and medium access control. The course will involve a good deal of programming in Python. Familiarity with programming, algorithms, and basic probability is expected. Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA.0310), and Discrete Mathematics (Math UA 120); or permission of the department.

• CSCI-UA.0480-​069
  (8024) Special Topics: Agile Software Development and DevOps  Amos Bloomberg TR 12:30-1:45PM Online
  

  Agile software development has come to describe a specific approach and toolset that allow for the requirements of a software project to change as a project progresses without disrupting schedules, budgets, and responsibilities. The field of DevOps, a portmanteau of development and operations, has introduced further processes and infrastructure to automate many of the tasks required in such development. Together, an Agile methodology and DevOps automation have increased the speed, robustness, and scalability with which software is developed today and have reduced the uncertainty of outcomes inherent in complex software development. Upon completion of this course, students will understand the core methodologies, technologies, and tools used in the software industry today.

• CSCI-UA.0480-​073
  (8025) Special Topics: Randomized Algorithms  Richard Cole MW 9:30-10:45AM Kimmel 808
  

  This course will look at randomization as a tool for designing more efficient algorithms. Examples include primality testing, string matching, stable matching with random preferences, parallel quicksort, hashing, bloom filters. The course will cover the necessary background in probability.

• CSCI-UA.0480-​074
  (20007) Special Topics: Quantum Computing  Nicholas Spooner TR 3:30-4:45PM CIWW 312
  

  Quantum computing is an interdisciplinary field that lies at the
  intersection of computer science, mathematics, and physics. This
  paradigm relies on principles of quantum mechanics, such as
  superposition and entanglement, to perform complex computation. This
  course aims to provide a self-contained, comprehensive introduction to
  quantum computing, focusing on the design and analysis of quantum
  algorithms, as well as covering topics in quantum information and
  quantum cryptography, such as quantum teleportation, quantum money, and
  nonlocal games.
   

  
  
  Prerequisites: A strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), discrete mathematics (e.g. NYU's MATH-UA.0120 Discrete Mathematics) and discrete probability (e.g., NYU’s MATH-UA 235 Probability and Statistics). Familiarity with theory of computation (e.g., NYU’s CSCI-UA.0310 Basic Algorithms or CSCI-UA.0453-001 Theory of Computation) is desirable. No background in physics is required.
• CSCI-UA.0521-​001 Undergraduate Research   STAFF -
  The student is supervised by a faculty member actively engaged in research, possibly leading to results publishable in the computer science literature. A substantial commitment to this work is expected. The research project may be one or two semesters, to be determined in consultation with the faculty supervisor. Students taking this course for honors in computer science are required to write an honors thesis. All other students need to submit a write-up of the research results at the conclusion of the project.
  
  Prerequisites: Permission of the department.
• CSCI-UA.0898-​001 Internship  STAFF -
  An internship in computer science is an excellent complement to formal course work. We strongly recommend that students have some practical training along with their classroom experience, so they can explore different career options and gain hands-on experience. An internship is for majors only, and students must have maintained an overall GPA of 3.0 and a computer science GPA of 3.5. The internship will be graded.
  
  Prerequisites: Restricted to declared computer science majors. Internship credit does not count toward major requirements, but does apply toward completion of the CAS degree.
• CSCI-UA.0998-​001 Independent Study   STAFF -
  

  Students majoring in the department are permitted to work on an individual basis under the supervision of a full-time faculty member in the department if they have maintained an overall GPA of 3.0 and a GPA of 3.5 in computer science and have a study proposal that is approved by the director of undergraduate studies. Students are expected to spend about three to six hours a week on their project.

  
  
  Prerequisites: Permission of the department. Does not satisfy the major elective requirement.

• *
  Indicates controlled enrollment (permission number required for registration). Contact your program advisor.
• **
  Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
• ***
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Classroom Locations