Course Schedule

Current semester Spring 2022, and future semesters are now available to view.

View undergraduate course grid View graduate course grid

Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1144-001
(2581) PAC II* Hasan Aljabbouli T 6:00-8:30PM CIWW 202

This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems

Prerequisites: CSCI-GA 1133 or departmental permission.
CSCI-GA.1144-002
(2918) PAC II Recitation Anish Mahishi R 7:10-8:00PM CIWW 202 Additional information: Recitation for Section 001

This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems

Prerequisites: CSCI-GA 1133 or departmental permission.
CSCI-GA.1170-001
DS-GA.1170
(2582) Fundamental Algorithms Chee Yap T 7:10-9:10PM Online

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
(3562) Fundamental Algorithms Recitation Eli Goldin R 8:10-9:00PM CIWW 109 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
DS-GA.1170
(3563) Fundamental Algorithms Recitation Zihan Feng R 8:10-9:00PM Online 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.1180-001
(3232) Mathematical Techniques For CS Applications Parijat Dube T 7:10-9:10PM CIWW 109

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
(2583) Programming Languages Cory Plock M 4:55-6:55PM CIWW 109

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-002
(2584) Programming Languages Recitation Jahnvi Arya R 7:10-8:00PM CIWW 109 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
(3564) Programming Languages Recitation Yashaswi Shah R 7:10-8:00PM Online Additional information: Recitation for Section 001

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2130-001
(2585) Compiler Construction Alexander Alekseyev M 7:10-9:10PM Online
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
(2586) 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
(3759) Operating Systems Hubertus Franke M 8:00-10:00AM Online
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
(2587) Data Communications and Networks Jean-Claude Franchitti M 7:10-9:10PM Online

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.2270-001
(26000) Computer Graphics Gizem Kayar R 4:55-6:55PM CIWW 202

Problems and objectives of computer graphics. Vector, curve, and character generation. Interactive display devices. Construction of hierarchical image list. Graphic data structures and graphics languages. Hidden-line problems; windowing, shading, and perspective projection. Curved surface generation display.

Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
CSCI-GA.2421-001
MATH-GA 2020-001
(2588) Numerical Methods II Leslie Greengard T 5:10-7:00PM CIWW 1302
Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.

Prerequisites: Corequisite: linear algebra.
CSCI-GA.2433-001
DS-GA.2433
(2589) Database Systems Zvi Kedem W 4:55-6:55PM CIWW 109
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
(20098) Realtime and Big Data Analytics Yang Tang W 4:55-6:55PM Online

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
(3579) Big Data Application Development Yang Tang R 7:10-9:10PM 60 Fifth Ave 150

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

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

Prerequisites: Prerequisites include experience with Hadoop, which commonly comes from having completed the Realtime and Big Data Analytics course. There will be a review of Apache Hadoop, but students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.2560-001
(3153) Artificial Intelligence Paul Bethe M 4:55-6:55PM Silv 207

There are many cognitive tasks that people do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course covers problem solving and state space search; automated reasoning; probabilistic reasoning; planning; and knowledge representation.
CSCI-GA.2565-001
(3709) Machine Learning Rajesh Ranganath T 7:10-9:10PM Silv 405

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.2566-001
(20090) Foundations Of Machine Learning* Mehryar Mohri T 4:55-6:55PM GCASL 288

This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are probability and general bounds; PAC model; VC dimension; perceptron, Winnow; support vector machines (SVMs); kernel methods; decision trees; boosting; regression problems and algorithms; ranking problems and algorithms; halving algorithm, weighted majority algorithm, mistake bounds; learning automata, Angluin-type algorithms; and reinforcement learning, Markov decision processes (MDPs).

Prerequisites: CSCI-GA 1180.
CSCI-GA.2572-001
(3725) Deep Learning Yann LeCun
Alfredo Canziani W 4:55-6:55PM Online

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
(3726) Deep Learning Lab Jiachen Zhu R 4:55-5:45PM Online 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
(20091) Natural Language Processing Adam Meyers T 4:55-6:55PM Online

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
(2943) Networks and Mobile Systems Anirudh Sivaraman R 7:10-9:10PM CIWW 201

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.2820-001
(3233) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Additional information: Not open to graduate Computer Science, Information Systems, CEI (Computing, Entrepreneurship, and Innovation), or Non-Degree students.

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
(3234) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001. Not open to graduate Computer Science, Information Systems, CEI (Computing, Entrepreneurship, and Innovation), or Non-Degree students.

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
(23376) 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
(23497) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM Online Additional information: Lab for Section 003

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
(20096) The Lean Launch Pad* Andrew Breen
Emily Baum R 10:15-12:15PM 16WP 106 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
(20097) The Lean Launch Pad Lab* Andrew Breen
Emily Baum R 12:30-1:20PM 16WP 106 Additional information: Lab for Section 001. Open only to students in the MS-CEI program.

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

This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands on learning on what it’s like to actually start a high tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.
CSCI-GA.2945-002
MATH-GA.2012-002
(2592) Advanced Topics In Numerical Analysis: High Performance Computing Benjamin Peherstorfer M 1:25-3:15PM CIWW 1302

This class will be an introduction to the fundamentals of parallel scientific computing. We will establish a basic understanding of modern computer architectures (CPUs and accelerators, memory hierarchies, interconnects) and of parallel approaches to programming these machines (distributed vs. shared memory parallelism: MPI, OpenMP, OpenCL/CUDA). Issues such as load balancing, communication, and synchronization will be covered and illustrated in the context of parallel numerical algorithms. Since a prerequisite for good parallel performance is good serial performance, this aspect will also be addressed. Along the way you will be exposed to important tools for high performance computing such as debuggers, schedulers, visualization, and version control systems. This will be a hands-on class, with several parallel (and serial) computing assignments, in which you will explore material by yourself and try things out. There will be a larger final project at the end. You will learn some Unix in this course, if you don't know it already. Prerequisites for the course are (serial) programming experience with C/C++ (I will use C in class) or FORTRAN, and some familiarity with numerical methods.
CSCI-GA.2945-003
MATH-GA.2012-003
(3569) Advanced Topics In Numerical Analysis: Randomized Numerical Linear Algebra Michael O'Neil
Jonathan Weare T 11:00-12:50PM CIWW 1302
Topics vary each semester.

Prerequisites: Topics determine prerequisites.
CSCI-GA.2945-004
MATH-GA.2012-004
(3570) Advanced Topics in Numerical Analysis: Convex and Non-Smooth Optimization Michael Overton M 5:10-7:00PM Online

Convex optimization problems have many important properties, including a powerful duality theory and the property that any local minimum is also a global minimum. Nonsmooth optimization refers to minimization of functions that are not necessarily convex, usually locally Lipschitz, and typically not differentiable at their minimizers. Topics in convex optimization that will be covered include duality, linear and semidefinite programming, CVX ("disciplined convex programming"), gradient and Newton methods, Nesterov's complexity bound, the alternating direction method of multipliers, the nuclear norm and matrix completion, the primal barrier method, primal-dual interior-point methods for linear and semidefinite programs. Topics in nonsmooth optimization that will be covered include subgradients and subdifferentials, Clarke regularity, and algorithms, including gradient sampling and BFGS, for nonsmooth, nonconvex optimization. Homework will be assigned, both mathematical and computational. Students may submit a final project on a pre-approved topic or take a written final exam.
CSCI-GA.3033-025
(25710) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran T 4:55-6:55PM Silv 405

This course serves as a capstone for the MSCS program.

In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn the architecture and the programming of GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.
CSCI-GA.3033-026
(3288) Special Topics: Cloud Computing Jean-Claude Franchitti R 7:10-9:10PM Silv 401

This course is a capstone course that introduces the latest Cloud computing, Artificial Intelligence (AI), and Web3 Decentralized Applications technologies that can be combined to create next-generation multi-cloud innovative Intelligent Autonomous Networked (IAN) business solutions. Students will learn about services provided on the top “Big Clouds”, namely Amazon AWS Cloud, Google Cloud, Microsoft Azure Cloud, IBM Cloud, Salesforce, and others. Related services provided on the Cloud and covered in the course include computing and hosting services, storage services, networking services, big data services, and machine/deep 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 next-generation mobile devices and social media 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, Edge and Fog computing and Internet of Things (IoT), Mobility/5G networking and Software Defined infrastructures, and various key aspects of digital security that are critical to ensure the safety of modern software. Throughout the course, students will develop their own Intelligent Autonomous Networked (IAN) business solutions using Cloud-based machine and deep learning and other services and capabilities as noted above. Sample platform and related application components will be provided as applicable to speed up the development process.
CSCI-GA.3033-029
(2971) Special Topics: Social Networks Bhubaneswar Mishra R 4:55-6:55PM 194M 204

Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. The World Wide Web enables information flows among vast number of humans; Facebook, and LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc, BitCoins, Ethereum, etc. allow parties to make financial and contractual transactions . These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorumsensing, swarming, flocking, social grooming, gossip, etc., and have evolved with the dynamics of evolutionary games (particularly Signaling Games). Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. Economic markets also look much more like networks than anonymous marketplaces. Firms interact with the suppliers and customers in a Web-like supply chains. Systemic risk in financial markets often results from the counterparty risks created within this financial network. This course will introduce the tools for the study of networks, and mechanism designed to interact strategically: SmartContractss, Costly Signaling, Credible and Noncredible Threats, Model Checking and Verification, Safety and Liveness, Recommenders and Verifiers, Deception, Market mechanisms (pricing and auction), Privacy and Trust… Exampled Systems: Bit-coins, Zcash, Ripple, Stellar, Ethereum, Solidity, Rholang, DAO (Distributed Companies), Cyber Security (M-coins), Information Finance Markets (WealthFront, etc.), Market Microstructure, ICO/STO/ITO, GDPR, OpenLaw, OpenZeplin, ...
CSCI-GA.3033-034
(3089) Special Topics: Multicore Processors: Architecture & Programming Mohamed Zahran W 4:55-6:55PM Silv 401

This is a capstone course.

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.3033-059
(2593) Special Topics: Big Data Science Anasse Bari W 4:55-6:55PM 194M 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
(3573) Special Topics: Geometric Modeling Daniele Panozzo R 4:55-6:55PM Online

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
(3575) Special Topics: Vision Meets Machine Learning Davi Geiger W 7:10-9:10PM 60 Fifth Ave 150

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
(3230) Special Topics: Big Data and ML Systems Aurojit Panda W 7:10-9:10PM GCASL 475

This class will discuss recent research into using cluster computing for data processing and machine learning. The focus of the course will be on understanding systems and performance challenges resulting from changing workloads, the emergence of new hardware (such as NVMe, SmartNICs, etc.), and the emergence of new security and privacy requirements, and understanding how systems address these challenges. The main focus of the course is on how these changes are exposed to programmers and users, and how they can be exploited for performance. Topics we plan to cover include:

* How both commercial and research systems are changing in response to changes in distributed storage including the wider adoption of blob stores like S3 and the availability of new hardware like Intel Optane.

* How scheduling is impacted by the move to machine learning and reinforcement learning workloads.

* How are people using serverless, and what technical challenges need to be addressed in these environments?

* Tracing and debugging in cluster computing: how are people debugging both correctness and performance problems in these environments.

* How are systems evolving to handle privacy requirements imposed by laws such as GDPR and CCPA?

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.3033-079
DS-GA.3001-013
(3711) Special Topics: Mathematics of Deep Learning Joan Bruna R 4:55-6:55PM 60 Fifth Ave 110

This graduate-level course explores some of the open mathematical challenges arising in the study of high-dimensional learning, and Deep Learning in particular. We will focus on three main topics: (i) approximation properties of neural networks in the high-dimensional regime (ii) non-convex optimization challenges and associated computational aspects, and (iii) generative modeling and links to geometry and optimal transport.

Prerequisites: Background and familiarity on high-dimensional probability, statistics, and/or harmonic analysis. Familiarity with general Machine Learning topics and basic notions of optimization.
CSCI-GA.3033-085
(3231) Special Topics: Cloud and Machine Learning I-Hsin Chung
Hao Yu R 4:55-6:55PM CIWW 201

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.3033-091
(20094) Special Topics: Introduction to Deep Learning Systems Parijat Dube F 7:10-9:10PM CIWW 109

The course will cover several topics related to Deep Learning (DL) systems and their performance.

Both algorithmic and system related building blocks of DL systems will be covered including DL training algorithms, network architectures, and best practices for performance optimization. We will study hyperparameter choices, scalable distributed DL training, Kubernetes based DL system stack on cloud, tools and benchmarks for performance evaluation of DL systems, transfer learning etc. Emphasis will be on getting working knowledge of tools and techniques for performance evaluation of DL systems. The students will gain practical experience working on different stages of DL life cycle, including model development, testing, and deployment. The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.

Prerequisites: An introductory machine learning course. Knowledge of python is preferred but not necessary.
CSCI-GA.3033-096
(24388) Special Topics: Data Analytics and Visualization in Healthcare Maria Lorena Carlo Unda T 7:10-9:10PM Online

This course provides foundational knowledge of data analytics and visualization applied to healthcare settings. Some topics include Health Information Technology (HIT) systems and components, use of R and Python programming languages for data analytics and visualization, best practices in data visualization, design of dashboards using Tableau, and descriptive and predictive analytics methods for healthcare.
CSCI-GA.3033-097
(24421) Special Topics: Virtual Reality* Kenneth Perlin W 4:55-6:55PM 60 Fifth Ave 350

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.3033-631
CS-GY.6313-SHAN
(24240) Special Topics: Information Visualization Xianbin Gu MW 7:30-8:45PM 1555 Century 207 Additional information: Only open to NYU Shanghai GoLocal students

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-918
ECE-GY.9183-SHAN
(24237) Special Topics: Project in Machine Learning Li Guo M 9:00-11:30AM 1555 Century 1505 Additional information: Only open to NYU Shanghai GoLocal students

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3140-001
(3578) Abstract Interpretation Patrick Cousot M 4:55-6:55PM 60 Fifth Ave C15

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
(20092) Applied Cryptography & Network Security Mazdak Zamani T 7:10-9:10PM CIWW 201
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.3205-002
(25631) Applied Cryptography & Network Security Mazdak Zamani F 4:55-6:55PM CIWW 109
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.3210-001
MATH-GA 2170-001
(20093) Introduction To Cryptography CANCELLED -
The primary focus of this course is on definitions and constructions of various cryptographic objects, such as pseudorandom generators, encryption schemes, digital signature schemes, message authentication codes, block ciphers, and others, time permitting. The class tries to understand what security properties are desirable in such objects, how to properly define these properties, and how to design objects that satisfy them. Once a good definition is established for a particular object, the emphasis will be on constructing examples that provably satisfy the definition. Thus, a main prerequisite of this course is mathematical maturity and a certain comfort level with proofs. Secondary topics, covered only briefly, are current cryptographic practice and the history of cryptography and cryptanalysis.

Prerequisites: Strong mathematical background
CSCI-GA.3812-001
(2594) Information Technology Projects * Evan Korth W 5:00-8:00PM Bobst 745

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
(3093) 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
(2595) 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
(2596) 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
(2597) 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
(2598) 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
(2599) 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
(2600) 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
(2601) 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
(2602) 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
(3377) 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
(2603) PhD Seminar: Formal Methods** Thomas Wies R 11:00-12:30PM 60 Fifth Ave 527
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
(2604) PhD Thesis Research ** STAFF -

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(3090) 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
(3091) 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.
MAINT-GA.4747-001
(2708) Maintenance of Matriculation (MS Students) STAFF -
Section 1 is for MS students. Section 4 is for non-supported PhD students.
MAINT-GA.4747-004
(2711) Maintenance of Matriculation (non-supported PhD students) STAFF -
Section 1 is for MS students. Section 4 is for non-supported PhD students.
CSCI-UA.0002-001
(7802) Intro To Computer Programming (No Prior Experience) Amanda Steigman MW 8:00-9:15AM CIWW 201

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
(9106) Intro To Computer Programming (No Prior Experience) Susan Liao TR 8:00-9:15AM CIWW 201

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
(7803) Intro To Computer Programming (No Prior Experience) Craig Kapp MW 9:30-10:45AM 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.0002-004
(7804) Intro To Computer Programming (No Prior Experience) Craig Kapp TR 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.0002-005
(7805) Intro To Computer Programming (No Prior Experience) Matthew Zeidenberg MW 11:00-12:15PM Silv 101a

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
(8686) Intro To Computer Programming (No Prior Experience) Julie Lizardo TR 11:00-12:15PM CIWW 201

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
(9902) Intro To Computer Programming (No Prior Experience) David Prager MW 12:30-1:45PM 25W 4th C-20

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
(9107) Intro To Computer Programming (No Prior Experience) Joshua Clayton TR 12:30-1:45PM Online

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-009
(8904) Intro To Computer Programming (No Prior Experience) Emily Zhao MW 2:00-3:15PM Kimmel 803

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
(9324) Intro To Computer Programming (No Prior Experience) Saadia Lgarch TR 2:00-3:15PM 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.0002-011
(9528) Intro To Computer Programming (No Prior Experience) Matthew Zeidenberg MW 3:30-4:45PM Silv 207

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
(9725) Intro To Computer Programming (No Prior Experience) Na'im Tyson TR 4:55-6:10PM 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.0002-013
(20825) Intro To Computer Programming (No Prior Experience) Michell Cardona TR 6:20-7:35PM Bobst 743

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
(20856) Intro to Computer Programming (Limited Prior Experience) Mauricio Arias Hernandez MW 11:00-12:15PM Kimmel 914

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

Prerequisites: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with some previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0003-002 Intro to Computer Programming (Limited Prior Experience) CANCELLED TR 8:00-9:15AM

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
(7806) Intro to Web Design & Computer Principles Alvaro Olsen MW 8:00-9:15AM GCASL 383
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
(7807) 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
(20826) Intro to Web Design & Computer Principles Joshua Clayton TR 9:30-10:45AM 60 Fifth Ave 110
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
(9904) Intro to Web Design & Computer Principles Mark Ramos MW 11:00-12:15PM Kimmel 808
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 Intro to Web Design & Computer Principles CANCELLED MW 12:30-1:45PM
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
(9071) Intro to Web Design & Computer Principles Alvaro Olsen MW 3:30-4:45PM 12WV L120
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
(7808) Database Design And Implementation Amos Bloomberg TR 12:30-1:45PM 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
(8622) Web Development And Programming Craig Kapp TR 12:30-1:45PM Silv 405

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
(7809) Intro To Computer Science Nader Nassar MW 8:00-9:15AM GCASL 475

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
(7810) Intro To Computer Science Duygu Ataman TR 8:00-9:15AM GCASL 383

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
(9108) Intro To Computer Science Anasse Bari TR 11:00-12:15PM Kimmel 808

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
(7811) Intro To Computer Science Candido Cabo MW 12:30-1:45PM GCASL 475

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
(8688) Intro To Computer Science Gizem Kayar TR 12:30-1:45PM GCASL 475

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
(8882) Intro To Computer Science Hilbert Gene Locklear MW 2:00-3:15PM 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.0101-007
(9325) Intro To Computer Science Amos Bloomberg TR 2:00-3:15PM CIWW 201

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
(9905) Intro To Computer Science Hilbert Gene Locklear MW 3:30-4:45PM 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.0101-009
(20827) Intro To Computer Science Duygu Ataman TR 3:30-4:45PM 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.0101-010
(26001) Intro To Computer Science Gizem Kayar MW 11:00-12:15PM Online

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
(20828) Data Structures Evan Korth MW 3:30-4:45PM 19UP 102

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
(20829) Data Structures - Recitation Diogo Vieira T 4:55-6:10PM Silv 408 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
(20830) Data Structures Anasse Bari TR 2:00-3:15PM Meyr 122

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
(20834) Data Structures - Recitation Vaibhav Mavi W 4:55-6:10PM 19UP 102 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
(20831) Data Structures Joanna Klukowska TR 9:30-10:45AM Cant 200

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

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-006
(20832) Data Structures - Recitation Swarna Swapna Ilamathy M 11:00-12:15PM 194M 304 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.0102-021
(20833) Data Structures - Recitation Sweta Bharti T 4:55-6:10PM Online Additional information: Online 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
(20835) Data Structures - Recitation Vivek Shah W 4:55-6:10PM Online Additional information: Online 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
(20836) Data Structures - Recitation Sumanth Reddy Muni M 11:00-12:15PM Online Additional information: Online 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
(20837) Data Structures - Recitation Lauren DiGiovanni M 8:00-9:15AM Silv 208 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.0102-063
(20838) Data Structures - Recitation Harshitha Raghavan Devarajan W 8:00-9:15AM Kimmel 914 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.0102-064
(20839) Data Structures - Recitation Swarna Swapna Ilamathy M 2:00-3:15PM Bobst 743 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.0102-065
(20840) Data Structures - Recitation Joseph Cappadona W 2:00-3:15PM Bobst 743 Additional information: In-person recitation for Section 5

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

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0201-001
(7816) Computer Systems Organization Douglas Moody MW 9:30-10:45AM GCASL 288

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
(7817) Computer Systems Organization - Recitation Harshitha Raghavan Devarajan T 9:30-10:45AM 12WP L120 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
(9059) Computer Systems Organization Benjamin Goldberg MW 11:00-12:15PM Meyr 122

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
(9060) Computer Systems Organization - Recitation Adithya Viswanathan R 4:55-6:10PM Silv 408 Additional information: In-person recitation for Section 3

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

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0201-005
(9188) Computer Systems Organization Hasan Aljabbouli TR 9:30-10:45AM Silv 520

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

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0201-006
(9189) Computer Systems Organization - Recitation Jaya Amit Sai Gurrala W 8:00-9:15AM 60 Fifth Ave 150 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
(20841) Computer Systems Organization Jean-Claude Franchitti MW 3:30-4:45PM 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
(20842) Computer Systems Organization - Recitation Jaya Amit Sai Gurrala T 4:55-6:10PM 12WP G08 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.0201-021
(9910) Computer Systems Organization - Recitation Shraddha Thyagarajan Iyer T 9:30-10:45AM Online Additional information: Online 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.0201-041
(9384) Computer Systems Organization - Recitation Samvid Zare R 4:55-6:10PM 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.0201-061
(9911) Computer Systems Organization - Recitation Rishika Pabba W 8:00-9:15AM Online Additional information: Online 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.0201-081
(20843) Computer Systems Organization - Recitation Yang He T 4:55-6:10PM Online Additional information: Online 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
(7818) Operating Systems Michael Walfish MW 3:30-4:45PM Silv 408

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
(9186) Operating Systems Allan Gottlieb TR 12:30-1:45PM 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.0202-003
(20844) Operating Systems Allan Gottlieb TR 3:30-4:45PM 60 Fifth Ave 150

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
(7819) Basic Algorithms Oded Regev TR 12:30-1:45PM Cant 101

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
(7820) Basic Algorithms - Recitation Min Jae Song M 12:30-1:45PM 12WP G08 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
(7821) Basic Algorithms Oded Regev TR 8:00-9:15AM 60 Fifth Ave 150

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
(8906) Basic Algorithms - Recitation Peter Fenteany W 8:00-9:15AM GCASL 461 Additional information: In-person 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
(9912) Basic Algorithms Seyed Hossein Nassajianmojarrad MW 3:30-4:45PM 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.0310-006
(9913) Basic Algorithms - Recitation Yifan Jin T 4:55-6:10PM GCASL 475 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.0310-021
(20845) Basic Algorithms - Recitation Harish Karthikeyan M 12:30-1:45PM Online Additional information: Online 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.0310-041
(20846) Basic Algorithms - Recitation Ishan Agarwal W 8:00-9:15AM 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.0310-061
(10617) Basic Algorithms - Recitation Xinyi Zhao T 4:55-6:10PM Online 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.0330-001
MATH-UA.0144
(8960) Introduction to Computer Simulation Brennan Sprinkle TR 11:00-12:15PM 194M 304

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
(8961) Introduction to Computer Simulation Guanchun Li TR 11:00-12:15PM 194M 304

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
(8687) Topics of General Interest: Drawing on the Web Joshua Clayton MW 3:30-4:45PM Online
Detailed descriptions available when topics are announced. Typical offerings include Computing in the Humanities and Arts and Introduction to Flash Programming. Does not count toward the computer science major.

Prerequisites: Topics determine prerequisites.
CSCI-UA.0381-001
(20858) Programming Tools for the Data Scientist Adam Meyers TR 12:30-1:45PM Online

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
(7822) Numerical Computing Michael Overton TR 11:00-12:15PM Online

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
(9017) Theory Of Computation Subhash Khot TR 9:30-10:45AM Kimmel 914

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
(20859) Applied Internet Technology Joseph Versoza MW 12:30-1:45PM Kimmel 914

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
(20860) Applied Internet Technology Joseph Versoza TR 9:30-10:45AM Online

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.0470-001
(20847) Object Oriented Programming Hasan Aljabbouli TR 2:00-3:15PM GCASL 461

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
(20848) 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
(9109) Intro To Machine Learning Pascal Wallisch MW 12:30-1:45PM CIWW 109

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

Prerequisites: Data Structures (CSCI-UA.102), Linear Algebra (MATH-UA.140) and Probability and Statistics (MATH-UA.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
CSCI-UA.0476-001
(9507) Processing Big Data for Analytics Applications Ann Malavet TR 8:00-9:15AM Silv 208

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

Prerequisites: (CSCI-UA 201) Computer Systems Organization and (CSCI-UA 310) Basic Algorithms. Familiarity with Linux commands and SQL will be helpful but is not required.
CSCI-UA.0478-001
MATH-UA.0243
(20849) Introduction To Cryptography Marshall Ball MW 9:30-10:45AM Kimmel 914
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
(9915) Data Management and Analysis Joseph Versoza MW 11:00-12:15PM Meyr 121

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
(20909) Data Management and Analysis Matthew Zeidenberg TR 3:30-4:45PM Silv 401

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

Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
CSCI-UA.0480-051
(20850) Special Topics: Parallel Computing Mohamed Zahran TR 2:00-3:15PM Silv 208
Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

Prerequisites: Topics determine prerequisites.
CSCI-UA.0480-057
(20851) Special Topics: Natural Language Processing Adam Meyers TR 9:30-10:45AM 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: Topics determine prerequisites.
CSCI-UA.0480-061
(20853) Special Topics: Open Source Software Development Joanna Klukowska TR 12:30-1:45PM Silv 403
Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

Prerequisites: Topics determine prerequisites.
CSCI-UA.0480-069
(20852) Special Topics: Agile Software Development and DevOps Amos Bloomberg MW 12:30-1:45PM Meyr 122
Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

Prerequisites: Topics determine prerequisites.
CSCI-UA.0480-072
(20854) Special Topics: Introduction to Robot Intelligence Lerrel Pinto MW 3:30-4:45PM 60 Fifth Ave c15
Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

Prerequisites: Topics determine prerequisites.
CSCI-UA.0490-001
(20855) Special Topics: Programming Languages Edward Yang MW 4:55-6:10PM Online

Covers topics in programming languages at an advanced level. Detailed course descriptions are available when topics are announced.

Prerequisites: Topics determine prerequisites.

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