Course Schedule

Current semester Spring 2025, and future semesters are now available to view.

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• CSCI-GA.1170-​001
  DS-GA.1170-001
  (2730) Fundamental Algorithms  Chee Yap 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
  DS-GA.1170-002
  (2731) Fundamental Algorithms Recitation  Bingwei Zhang R 5:55-6:45PM Meyr 122 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
  (2732) Fundamental Algorithms Recitation  Bingwei Zhang F 4:55-5:45PM 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-​004
  (15617) Fundamental Algorithms  Yevgeniy Dodis 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-​005
  (15618) Fundamental Algorithms Recitation  TBA R 3:45-4:35PM 60 Fifth Ave 110 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
  (2733) Mathematical Techniques For CS Applications  Parijat Dube R 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
  (2734) Programming Languages  Benjamin Goldberg 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
  (2735) Programming Languages Recitation  TBA R 7:10-8:00PM Silv 206 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
  (15679) Programming Languages Recitation  TBA F 11:15-12:05PM Meyr 122 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-​004
  (15624) Programming Languages  Cory Plock T 4:55-6:55PM Kimmel 808
  

  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-​005
  (15626) Programming Languages Recitation  TBA F 4:55-5:45PM Silv 401 Additional information: Recitation for Section 004
  

  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
  (2736) 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
  (2737) 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
  (2738) 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
  (2739) Data Communications and Networks  Jean-Claude Franchitti M 7:10-9:10PM 60 Fifth Ave 150
  

  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.2421-​001
  MATH-GA 2020-001
  (17313) Numerical Methods II  Michael O'Neil MW 11:00-12:15PM CIWW 1302
  

  This course will cover fundamental methods that are essential for the numerical solution of differential equations. It is intended for students familiar with ODE and PDE and interested in numerical computing; computer programming assignments form an essential part of the course. The course will introduce students to numerical methods for (1) nonlinear equations, Newton's method; (2) ordinary differential equations, Runge-Kutta and multistep methods, convergence and stability; (3) finite difference, finite element and integral equation methods for elliptic partial differential equations; (4) fast solvers, multigrid methods; and (5) parabolic and hyperbolic partial differential equations.
  
  Text: LeVeque, R. (2007). Classics in Applied Mathematics [Series]. Finite Difference Methods for Ordinary and Partial Differential Equations. Philadelphia, PA: Society for Industrial and Applied Mathematics

  
  
  Prerequisites: Numerical linear algebra, elements of ODE and PDE.
• CSCI-GA.2433-​001
  DS-GA.2433-001
  (2742) Database Systems  Matthew Zeidenberg W 4:55-6:55PM 60 Fifth Ave 150
  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
  (13588) 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.2437-​001
  (13589) 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.2565-​001
  (2744) Machine Learning   Matus Telgarsky W 7:10-9:10PM CIWW 109
  

  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.2569-​001
  DS-GA.1005-001
  (17537) Inference and Representation  Joan Bruna M 2:00-3:40PM 60 Fifth Ave 150
  

  This course covers graphical models, causal inference, and advanced topics in statistical machine learning.

  
  
  Prerequisites: DS-GA 1004
• CSCI-GA.2569-​002
  DS-GA.1005-002
  (17538) Inference and Representation Lab  STAFF T 1:30-2:20PM Silv 520 Additional information: Lab for Section 001
  

  This course covers graphical models, causal inference, and advanced topics in statistical machine learning.

  
  
  Prerequisites: DS-GA 1004
• CSCI-GA.2572-​001
  DS-GA.1008-001
  (17532) Deep Learning  Mengye Ren M 4:55-6:35PM Silv 207
  

  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
  (17533) Deep Learning Lab  TBA T 5:55-6:45PM 12WP G08 Additional information: Lab for Section 001
  

  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.2590-​001
  (15657) Natural Language Processing  He He W 4:55-6:55PM CIWW 312
  

  Natural Language Processing applies computational and linguistic knowledge to the processing of natural languages (English, Chinese, Spanish, Japanese). Applications include: machine translation, information extraction, information retrieval, and others. On the one hand, the class will include the modeling and representation of linguistic phenomena. On the other, It will cover methods for applying this knowledge using both manual rules and machine learning. Sample topics include: formal languages, hmm, part of speech tagging, vector-based methods, shallow and full parsing, semantic role labeling, information extraction and machine translation. Students will complete shared tasks (POS-tagging, Information Extraction, etc.) and group final projects.  There will also be a midterm exam and several small written assignments.

  Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.

  
  
  Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
• CSCI-GA.2620-​001
  (15660) Networks and Mobile Systems  Anirudh Sivaraman W 10:15-12:15PM GCASL 361
  

  This is a capstone course. A course in computer networks and large-scale distributed systems. Teaches the design and implementation techniques essential for engineering both robust networks and Internet-scale distributed systems. The goal is to guide students so they can initiate and critique research ideas in networks and distributed systems and implement and evaluate a working system that can handle a real-world workload. Topics include routing protocols, network congestion control, wireless networking, peer-to-peer systems, overlay networks and applications, distributed storage systems, and network security.

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
• CSCI-GA.2621-​001
  (12840) 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
  (12562) 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
  (12563) 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
  (12564) 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
  (12565) 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
  (12566) 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
  (12567) 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.3033-​026
  (7633) Special Topics: Cloud Computing  Jean-Claude Franchitti R 7:10-9:10PM CIWW 312
  

  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.

  
  
  Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
• CSCI-GA.3033-​034
  (19062) Special Topics: Multicore Processors: Architecture & Programming  Mohamed Zahran W 12:30-2:30PM Online
  

  This course serves as a capstone for the MSCS program.
  
  The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable. 

• CSCI-GA.3033-​052
  (7636) 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
  (7637) 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.  

  
  
  Prerequisites: Strong programming skills in the Java language and fundamentals of linear algebra.
• CSCI-GA.3033-​071
  (7638) Special Topics: Geometric Modeling  Daniele Panozzo M 4:55-6:55PM 60 Fifth Ave C10
  

  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
  (7639) Special Topics: Vision Meets Machine Learning  Davi Geiger W 10:15-12:15PM 60 Fifth Ave C15
  

  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
  (7640) Special Topics: Big Data and ML Systems  Lakshminarayanan Subramanian T 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.

  
  
  Prerequisites: The class does require an undergraduate-level knowledge in operating systems, programming languages, algorithms, linear algebra, probability and statistics. If you are new to these concepts, then this class may be advanced for you.
• CSCI-GA.3033-​085
  (7642) Special Topics: Cloud and Machine Learning  Hao Yu R 4:55-6:55PM CIWW 317
  

  This is a capstone course for the MSCS program. 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
  (7643) Special Topics: Deep Decision Making & Reinforcement Learning  Lerrel Pinto F 4:55-6:55PM 60 Fifth Ave C15
  

  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-​097
  (15673) Special Topics: Virtual Reality*  Kenneth Perlin T 4:55-6:55PM 60 Fifth Ave 446
  

  Students will learn how to create shared interactive experiences in Virtual Reality (VR), using an open-source WebXR platform. This is a hands-on projects based course. Topics include design and implementation of interactive tools for collaborative modeling and animation, with a focus on graphical user interface (GUI) design for VR.

  Apply for a seat in the course: https://forms.gle/ak6XsSX32XvkszsW8

  
  
  Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
• CSCI-GA.3033-​099
  DS-GA 3001-009
  (11518) Special Topics in DS: Introduction to Computer Vision  Jean Ponce M 4:55-6:35PM 60 Fifth Ave 150
  

  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-​100
  (15759) Special Topics: Public Interest Technology  Sunoo Park W 12:30-2:30PM 60 Fifth Ave C15
  

  Technologists are in a unique position to utilize their knowledge and skills in the public interest. This course is an introduction to the theory and practice of public interest technology, with substantial projects that will give students the opportunity to gain experience working with external stakeholders on real public-interest issues. For example, students will have the opportunity to work on a mission-driven advocacy project on a current tech policy issue in the US, e.g., related to surveillance, privacy, or AI. Collaborating organizations that students may get to work with include the ACLU, the Policing Project at NYU Law School, the Knight First Amendment Institute at Columbia University, the Surveillance Technology Oversight Project (STOP), and more.

  The aim of this course is to elaborate strategies of engagement with broader multidisciplinary social issues related to technology and the public interest. Thus the course consists of two parts: (1) a regular class meeting covering theories, practical approaches, and case studies of public interest technology, and (2) a project-based practicum in which students will work in groups with partners (individuals or organizations) to address a specific public interest issue. This course has no exam; grading will be based on project work and class participation. A majority of students' time will be spent collaboratively in pairs/groups for the project practicum, with weekly instructor-team meetings.

   

  Admission to this course is by application only, via this form.  Applications will be evaluated on a rolling basis starting November 11, 2024.

• CSCI-GA.3033-​102
  (15671) Special Topics: Learning with Large Language and Vision Models  Saining Xie M 4:55-6:55PM CIWW 101
  

  This course delves into the recent advancements in deep learning made possible by the development of large pre-trained language and vision models. These models have revolutionized the field, demonstrating remarkable capabilities in generation and comprehension, and forming the foundation of cutting-edge AI systems across a wide range of applications. The research-oriented course encompasses a variety of topics in language, vision, and multi-modal modeling. The course structure includes a combination of lectures, paper review sessions, and hands-on projects throughout the semester.

  
  
  Prerequisites: Students must have completed courses in either deep learning, machine learning, or computer vision and demonstrate proficiency in Python programming.
• CSCI-GA.3033-​106
  (7646) 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
  (7647) Special Topics: Cryptography of Blockchains  Benedikt Bünz M 12:30-2:30PM 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-​109
  (7649) 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-​114
  CS-GY 9223-I
  (34377) Special Topics: Recent Developments in Algorithm Design  Anupam Gupta R 2:00-4:30PM CIWW 202
  

  The course covers several recent developments in algorithm design: e.g., recent trends in data structures, fast algorithms for shortest paths, matchings and flows, online and approximation algorithms, stochastic probing and sequencing problems, and algorithms for numerical linear algebra. Each of these topics will be covered in a set of 2-3 lectures by NYU faculty who are specialists in the respective areas.

  
  
  Prerequisites: Graduate level knowledge of algorithms required. For example, Honors Analysis of Algorithms, or similar graduate-level Algorithms courses.
• CSCI-GA.3033-​120
  (18972) Special Topics: Interactive Proofs  Michael Chapman R 10:15-12:15PM CIWW 202
  

  Interactive proofs played a major role in various developments in theoretical computer science and cryptography. In this course, we aim to study these computational models via the complexity theoretic lens, and in particular discuss the PCP Theorem and applications of it to  Hardness of Approximation results. If time allows, we will discuss the quantum analogues of these computational models, and their relation to the Approximation Theory of Algebraic objects in general and Connes' Embedding Problem in particular.

  
  
  Prerequisites: Linear algebra. Some knowledge in Complexity theory, Probability Theory (over finite spaces), and Abstract Algebra (in particular, Group Theory) will be helpful but not mandatory.
• CSCI-GA.3033-​121
  (34260) Special Topics: Programming Parallel Algorithms  Samuel Westrick M 10:15-12:15PM 60 Fifth Ave C15
  

  This course considers the challenge of converting high-level algorithmic ideas into efficient parallel code. We will see how this challenge is greatly simplified by modern programming languages and programming techniques, especially functional programming techniques which make it possible to quickly develop efficient, scalable, and correct implementations. Prior experience with functional programming is helpful but not required. Students will learn how to design, analyze, implement, and evaluate the performance of parallel algorithms across a variety of problem domains (e.g., graph analysis, computational geometry, numerical algorithms, image processing, etc.). In the second half of the course, students will complete a parallel programming project of their own design.

  
  
  Prerequisites: CSCI-GA.2110 Programming Languages or CSCI-GA.3110 Honors Programming Languages
• CSCI-GA.3033-​934
  (34512) Special Topics: Multicore Processors: Architecture & Programming Recitation  TBA F 12:30-1:20PM CIWW 101 Additional information: Recitation for Section 034
  

  This is the recitation for section 034. Refer to the lecture for description.

• CSCI-GA.3033-​999
  (11519) Special Topics: Introduction to Computer Vision Recitation  STAFF R 4:55-5:45PM Silv 401 Additional information: Recitation for Section 099
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​9483
  ECE-GY.9483
  (35721) Special Topics: Efficient AI and Hardware Accelerator Design  Sai Qian Zhang W 5:00-7:30PM Jacobs 775
  

  The course will focus on recent advancements in the design of efficient neural networks, specifically on how to create and optimize AI models for improved performance, scalability, and resource efficiency. Students will explore key techniques like model compression, pruning, quantization, and model distillation for CNN, RNN, Transformer and LLM, aimed at reducing computational complexity and memory usage while maintaining accuracy. Additionally, the course will cover efficient training and inference methods, including distributed computing, parallelism, and low-precision computation, which are crucial for deploying AI on resource-limited devices such as smartphones or edge computing systems. Students will also study advanced hardware architectures of AI accelerators, gaining insights into the hardware implementation of neural network computations on these specialized systems.  

  
  
  Prerequisites: This course is open to both PhD and MS students. Prerequisites include prior knowledge of computer architecture (CSCI-UA.0201-001 or equivalent) and basic understanding of deep learning (CSCI-GA.2572-001 or equivalent).
• CSCI-GA.3140-​001
  (12841) 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
  (2746) Applied Cryptography & Network Security  Mazdak Zamani W 4:55-6:55PM 60 Fifth Ave 110
  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
  (8726) 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
  (2747) 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
  (2748) 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
  (2749) 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
  (2750) 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
  (2751) 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
  (2752) 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
  (2753) 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
  (2754) 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
  (2755) 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
  (2756) PhD Seminar: Cryptography **  Yevgeniy Dodis T 12:30-3:15PM 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
  (2757) 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
  (2758) PhD Thesis Research **  STAFF -
  
  
  Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
• CSCI-GA.3870-​001
  (2759) 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
  (2760) 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
  (16819) 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
  (16820) 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
  (16821) Intro To Computer Programming (No Prior Experience)  Craig Kapp MW 9:30-10:45AM 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-​004
  (16822) Intro To Computer Programming (No Prior Experience)  Waqar Aqeel 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
  (16823) Intro To Computer Programming (No Prior Experience)  Karl Rosenberg 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
  (16824) Intro To Computer Programming (No Prior Experience)  Joshua Clayton TR 11:00-12:15PM Silv 411
  

  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
  (16825) Intro To Computer Programming (No Prior Experience)  David Prager MW 12:30-1:45PM Kimmel 808
  

  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
  (16826) Intro To Computer Programming (No Prior Experience)  Emily Zhao TR 12:30-1:45PM Kimmel 808
  

  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
  (16827) Intro To Computer Programming (No Prior Experience)  Erik Larsen MW 2:00-3:15PM Silv 401
  

  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
  (16828) Intro To Computer Programming (No Prior Experience)  Karl Rosenberg TR 2:00-3:15PM Silv 401
  

  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
  (16829) Intro To Computer Programming (No Prior Experience)  CANCELLED -
  

  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
  (16830) Intro To Computer Programming (No Prior Experience)  Khye Borg Liew MW 2:00-3:15PM 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-​013
  (16831) Intro To Computer Programming (No Prior Experience)  Na'im Tyson 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
  (16832) 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
  (16915) Intro to Computer Programming (Limited Prior Experience)  Adam Meyers TR 11:00-12:15PM Silv 207
  

  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
  (16833) 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
  (16834) 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
  (16835) Intro to Web Design & Computer Principles  Mark Ramos MW 11:00-12:15PM CIWW 312
  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
  (16836) Intro to Web Design & Computer Principles  Joshua Clayton TR 3:30-4:45PM Tisch LC9
  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
  (16837) Intro to Web Design & Computer Principles  Emily Zhao MW 12:30-1:45PM Silv 401
  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
  (16838) Intro to Web Design & Computer Principles  Alvaro Olsen MW 3:30-4:45PM Silv 411
  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
  (16839) Database Design And Implementation  David Prager MW 8:00-9:15AM Silv 408
  

  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
  (16841) Web Development And Programming  Craig Kapp MW 2:00-3:15PM Silv 414
  

  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
  (16842) Web Development And Programming  Emily Zhao TR 3:30-4:45PM Silv 401
  

  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
  (16843) Intro To Computer Science  Nader Nassar MW 8:00-9:15AM GCASL 279
  

  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
  (16844) Intro To Computer Science  Gabriel Provencher Langlois TR 8:00-9:15AM Silv 208
  

  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
  (16845) Intro To Computer Science  Tobias Blickhan MW 9:30-10:45AM 60 Fifth Ave 110
  

  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
  (16846) 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-​005
  (16847) Intro To Computer Science  Gizem Kayar 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-​006
  (16848) Intro To Computer Science  Hilbert Gene Locklear MW 12:30-1:45PM Kimmel 803
  

  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
  (16849) Intro To Computer Science  Candido Cabo MW 2:00-3:15PM Kimmel 803
  

  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
  (16850) Intro To Computer Science  Hilbert Gene Locklear TR 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-​009
  (16851) Intro To Computer Science  Anasse Bari TR 2:00-3:15PM 60 Fifth Ave C10
  

  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
  (16852) 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-​011
  (16853) 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
  (16854) Data Structures  Joanna Klukowska MW 8:00-9:15AM GCASL C95
  

  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
  (16855) Data Structures - Recitation  TBA 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
  (16856) Data Structures  Anasse Bari TR 3:30-4:45PM 60 Fifth Ave C15
  

  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
  (16857) Data Structures - Recitation  TBA F 11:00-12:15PM 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-​005
  (16858) Data Structures  Evan Korth TR 12:30-1:45PM Silv 405
  

  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
  (16859) Data Structures - Recitation  TBA 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-​007
  (16860) Data Structures  Max Sklar 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
  (16861) Data Structures - Recitation  TBA F 9:30-10:45AM 7E12 LL23 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
  (16862) 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
  (16863) Data Structures - Recitation  TBA F 2:00-3:15PM 12WP L113 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
  (16864) Data Structures - Recitation  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
  (16865) Data Structures - Recitation  TBA F 12:30-1:45PM Bobst LL150 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
  (16866) Data Structures - Recitation  TBA 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
  (16867) Data Structures - Recitation  TBA 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
  (16868) Data Structures - Recitation  TBA 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
  (16869) Data Structures - Recitation  TBA 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
  (16870) Data Structures - Recitation  TBA 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
  (16871) Data Structures - Recitation  TBA 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
  (16872) Data Structures - Recitation  TBA 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.0102-​062
  (16873) Data Structures - Recitation  TBA F 12:30-1:45PM CIWW 317 Additional information: In-person recitation for Section 6
  

  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
  (16874) 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
  (16875) Computer Systems Organization - Recitation  TBA F 9:30-10:45AM 60 Fifth Ave 150 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
  (16876) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Online
  

  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
  (16877) Computer Systems Organization - Recitation  TBA F 9:30-10:45AM Online Additional information: Online 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
  (16878) Computer Systems Organization  TBA MW 3:30-4:45PM Kimmel 808
  

  This section is a fast paced version of the regular CSO class. Students who are considering research in their senior year or graduate school should take this class.
  
  This course aims to give students an understanding of what's "under the hood" in a modern computer system. Take this class to boost your confidence on a computer and to understand how everything works like never before! We'll cover basic topics including how machines represent and execute programs, how user program interact with the OS, as well as advanced topics including how to write networked and concurrent programs. These topics are supported by a series of challenging lab assignments in C.

  
  
  Prerequisites: You need to be comfortable with programming (e.g. in Java) and the UNIX environment.
• CSCI-UA.0201-​006
  (16879) Computer Systems Organization - Recitation  TBA F 11:00-12:15PM Silv 401 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
  (16880) Computer Systems Organization  Gizem Kayar MW 2:00-3:15PM CIWW 109
  

  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
  (16881) Computer Systems Organization - Recitation  TBA F 11:00-12:15PM Silv 207 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
  (16882) Operating Systems  Michael Walfish MW 3:30-4:45PM CIWW 109
  

  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
  (16883) 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.0202-​003
  (18081) Operating Systems  Jocelyn Chen TR 12:30-1:45PM CIWW 312
  

  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
  (16884) Basic Algorithms  Nir Bitansky MW 9:30-10:45AM 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
  (16885) Basic Algorithms - Recitation  Rachit Garg F 9:30-10:45AM Tisch LC11 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
  (16886) 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
  (16887) Basic Algorithms - Recitation  TBA 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
  (16888) Basic Algorithms  Jiaxin Guan MW 12:30-1:45PM Silv 414
  

  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
  (16889) Basic Algorithms - Recitation  TBA F 12:30-1:45PM Silv 401 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
  (16890) Basic Algorithms  Rotem Oshman MW 3:30-4:45PM 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-​008
  (16891) Basic Algorithms - Recitation  TBA F 8:00-9:15AM 60 Fifth Ave 150 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
  (16892) Basic Algorithms - Recitation  TBA F 11:00-12:15PM Bobst LL150 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
  (16893) Basic Algorithms - Recitation  Rachit Garg F 2:00-3:15PM Bobst LL150 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
  (16909) Introduction to Computer Simulation  Charles Peskin TR 11:00-12:15PM 194M 203
  

  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
  (16910) Introduction to Computer Simulation  Claire Nicole Valva F 11:00-12:15PM Silv 406 Additional information: In-person recitation for Section 1
  

  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
  (16894) Topics of General Interest: Drawing on the Web  Joshua Clayton MW 3:30-4:45PM CIWW 312
  

  In this course you will learn how to develop multimedia applications for both the desktop and the web. The course focuses on general programming techniques with an emphasis on the production of interactive graphical environments. In addition, a number of advanced topics will be covered including image processing, video games, simulations, basic computer vision, augmented reality (AR), virtual reality (VR) and data visualization techniques.

  
  
  Prerequisites: CSCI-UA.0002 and CSCI-UA.0004, or permission of Instructor
• CSCI-UA.0381-​001
  (16917) Programming Tools for the Data Scientist  Hasan Aljabbouli TR 3:30-4:45PM 60 Fifth Ave 150
  

  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
  (16895) Numerical Computing   Michael Overton 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.0453-​001
  (16896) Theory Of Computation   Richard Cole TR 9:30-10:45AM CIWW 312
  

  A mathematical approach to studying topics in computer science, such as regular languages and some of their representations (deterministic finite automata, nondeterministic finite automata, regular expressions) and proof of nonregularity. Context-free languages and pushdown automata; proofs that languages are not context-free. Elements of computability theory. Brief introduction to NP-completeness.

  
  
  Prerequisites: Basic Algorithms (CSCI-UA 310) and Computer Systems Organization (CSCI-UA.201).
• CSCI-UA.0467-​001
  (16918) 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
  (16919) 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
  (16923) Natural Language Processing  Adam Meyers TR 8:00-9:15AM 12WV G08
  

  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
  (16897) Object Oriented Programming  Hasan Aljabbouli MW 9:30-10:45AM Online
  

  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
  (16898) 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
  (16911) Fundamentals of Machine Learning  Pascal Wallisch MW 11:00-12:15PM GCASL C95
  

  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
  (16912) Fundamentals of Machine Learning - Recitation  Zhe Zeng F 9:30-10:45AM Silv 405 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
  (16913) Fundamentals of Machine Learning - Recitation  Zhe Zeng F 2:00-3:15PM Silv 405 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
  (16899) Software Engineering  Amos Bloomberg TR 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.0475-​001
  (19289) Predictive Analytics  Ahmad Emad MW 8:00-9:15AM CIWW 317
  

  Predictive analytics is the art and science of extracting useful information from historical data and present data for the purpose of predicting future trends. In this course, students will be introduced to the phases of the analytics life-cycle and will gain an understanding of a variety of tools and machine learning algorithms for analyzing data and discovering forward insights. Several techniques will be introduced including: data preprocessing techniques, data reduction algorithms, data clustering algorithms, data classification algorithms, uplifting algorithms, association rules, data mining algorithms, recommender systems, and more. This course aims to provide students with skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA 310), and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148).
• CSCI-UA.0478-​001
  MATH-UA.0243
  (16901) Introduction To Cryptography  Marshall Ball MW 9:30-10:45AM GCASL 288
  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
  (16920) 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
  (16921) Data Management and Analysis  Matthew Zeidenberg TR 11:00-12:15PM Kimmel 808
  

  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
  (16922) Data Management and Analysis  Matthew Zeidenberg MW 3:30-4:45PM 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.0480-​041
  (16902) 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.

  
  
  Prerequisites: CSCI-UA 201 Computer Systems Organization, CSCI-UA-310 (Basic Algorithms) and MATH-UA-140 (Linear Algebra), or departmental consent.
• CSCI-UA.0480-​051
  (16904) Special Topics: Parallel Computing  Mohamed Zahran TR 2:00-3:15PM Online
  

  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: Computer System Organization (CSCI-UA.0201) with a grade of C or better
• CSCI-UA.0480-​061
  (16905) Special Topics: Open Source Software Development  Joanna Klukowska MW 12:30-1:45PM 60 Fifth Ave 110
  

  This course prepares students to become active participants in open source projects. It begins with an overview of the philosophy and brief history of open source development, followed by an in-depth look at different types of open source projects and the study of various tools involved in open source development. In particular, it covers the collaborative nature of open source projects, community structure, version control systems, licensing, intellectual property, types of contributions (programming and non-programming) and the tool-chains that enable such contributions. The students are expected to contribute to existing open source projects.

  
  
  Prerequisites: CSCI-UA.0201 with a grade of C or better
• CSCI-UA.0480-​069
  (16906) Special Topics: Agile Software Development and DevOps  Amos Bloomberg MW 11:00-12:15PM 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.

  
  
  Prerequisites: CSCI-UA.0201 with a grade of C or better

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Classroom Locations