Course Schedule

Current semester Fall 2023, and future semesters are now available to view.

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• CSCI-GA.1133-​001
  (22849) PAC I*  CANCELLED -
  

  An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field. Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming. The primary programming language used in the course will be Java. Students should expect an average of 12-16 hours of programming and related course work per week.

• CSCI-GA.1133-​002 PAC I Recitation  CANCELLED -
  

  An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field. Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming. The primary programming language used in the course will be Java. Students should expect an average of 12-16 hours of programming and related course work per week.

• CSCI-GA.1170-​001
  (2346) Fundamental Algorithms  Yevgeniy Dodis T 4:55-6:55PM Meyr 121
  

  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
  (2347) Fundamental Algorithms Recitation  Peter Hall R 5:55-6:45PM Silv 408 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
  (26541) Fundamental Algorithms  Ernest Davis T 4:55-6:55PM CIWW 312
  

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​006
  (20528) Fundamental Algorithms Recitation  Eli Goldin F 4:55-5:45PM Silv 520 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-​007
  (26542) Fundamental Algorithms Recitation  Sihyun Lee R 5:55-6:45PM CIWW 312 Additional information: Recitation for Section 003
  

  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
  (2937) Mathematical Techniques For CS Applications  Parijat Dube W 7:10-9:10PM CIWW 101
  

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

• CSCI-GA.2110-​001
  (2348) Programming Languages  Benjamin Goldberg W 7:10-9:10PM 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
  (2349) Programming Languages Recitation  Devora Chait-Roth R 4:55-5:45PM 19UP 102 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
  (2780) Programming Languages  Cory Plock T 4:55-6:55PM Bobst LL138
  

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

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2110-​005
  (3247) Programming Languages Recitation  Animesh Ramesh F 5:55-6:45PM Silv 414 Additional information: Recitation for Section 003
  

  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-​006
  (3089) Programming Languages Recitation  Chaitanya Agarwal F 11:15-12:05PM Silv 206 Additional information: Recitation for Section 001
  

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

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2112-​001
  MATH-GA 2043-001
  (2350) Scientific Computing   Georg Stadler R 5:10-7:00PM CIWW 1302
  Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.
  
  Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
• CSCI-GA.2250-​001
  (2351) 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.2262-​001
  (2707) Data Communications and Networks  Jean-Claude Franchitti R 7:10-9:10PM Silv 401
  

  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
  (2813) Computer Graphics   Gizem Kayar W 7:10-9:10PM 5WP 101
  

  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.2271-​001
  (2781) Computer Vision   Rob Fergus R 7:10-9:10PM 19UP 102
  

  Basic techniques of computer vision and image processing. General algorithms for image understanding problems. Study of binary image processing, edge detection, feature extraction, motion estimation, color processing, stereo vision, and elementary object recognition. Mathematical, signal processing, and image processing tools. Relation of computer vision algorithms to the human visual system.

  
  
  Prerequisites: CSCI-GA 1170
• CSCI-GA.2420-​001
  MATH-GA 2010-001
  (2352) Numerical Methods I   Benjamin Peherstorfer M 5:10-7:00PM CIWW 102
  Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.
  
  Prerequisites: Corequisite: linear algebra.
• CSCI-GA.2433-​001
  DS-GA.2433-001
  (2704) Database Systems  Jean-Claude Franchitti R 4:55-6:55PM Meyr 122
  Database system architecture. Modeling an application and logical database design. The relational model and relational data definition and data manipulation languages. Design of relational databases and normalization theory. Physical database design. Concurrency and recovery. Query processing and optimization.
• CSCI-GA.2434-​001
  (2353) Advanced Database Systems  Dennis Shasha R 4:55-6:55PM CIWW 101
  This is a capstone course emphasizing large-scale database systems. This course studies the internals of database systems as an introduction to research and as a basis for rational performance tuning. Topics include concurrency control, fault tolerance, operating system interactions, query processing, and principles of tuning. Database capstone projects involve topics such as design, concurrency control, interactions, and tuning. These projects include some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
• CSCI-GA.2436-​001
  (3044) Realtime and Big Data Analytics  Yang Tang W 7:10-9:10PM Silv 411
  

  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
  (3444) Big Data Application Development  Yang Tang M 7:10-9:10PM Bobst LL150
  

  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
  (3211) Artificial Intelligence   Paul Bethe M 4:55-6:55PM Bobst LL150
  

  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
  (3448) Machine Learning   Mengye Ren T 4:55-6:55PM Silv 414
  

  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
  (23190) Foundations Of Machine Learning*  Mehryar Mohri T 4:55-6:55PM CIWW 317
  

  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
  (3504) Deep Learning  Yann LeCun
  Alfredo Canziani W 4:55-6:55PM 12WV G08
  

  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
  (3515) Deep Learning Lab  Alfredo Canziani R 4:55-5:45PM 19W4 101
  

  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
  (3065) Natural Language Processing  Adam Meyers T 4:55-6:55PM CIWW 101
  

  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.2630-​001
  (3220) Foundations of Networks and Mobile Systems*  Taylor Valore T 10:00-11:50AM KMEC 2-60 Additional information: Open only to students in the Stern Tech-Focused MBA program
  

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2630-​002
  (3221) Foundations of Networks and Mobile Systems Lab*  Taylor Valore T 9:00-9:50AM KMEC 2-60 Additional information: Open only to students in the Stern Tech-Focused MBA program
  

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2630-​003
  (22708) Foundations of Networks and Mobile Systems*  Lakshminarayanan Subramanian W 10:15-12:15PM 60 Fifth Ave C15 Additional information: Update for Fall 2023, MSCS students can request a permission code by emailing graduate@cs.nyu.edu
  

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2630-​004
  (22709) Foundations of Networks and Mobile Systems Lab*  Lakshminarayanan Subramanian W 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2810-​001
  (3422) Design and Innovation*  Gary Zamchick F 10:15-12:15PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

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

  This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

  
  
  Prerequisites: None
• CSCI-GA.2810-​002
  (3423) Design and Innovation Lab*  Gary Zamchick F 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

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

  This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

  
  
  Prerequisites: None
• CSCI-GA.2820-​001
  (2936) DevOps and Agile Methodologies  John Rofrano T 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-​002
  (2952) DevOps and Agile Methodologies Lab  John Rofrano T 7:10-8:00PM Online
  

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

• CSCI-GA.2820-​003
  (3253) 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
  (3254) DevOps and Agile Methodologies Lab  John Rofrano W 7:10-8:00PM Online
  

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

• CSCI-GA.2945-​002
  MATH-GA 2011-002
  (3219) Advanced Topics in Numerical Analysis: Computational Methods for PDE  Jonathan Goodman
  Georg Stadler T 11:00-12:50PM CIWW 1302
  Topics vary each semester.
  
  Prerequisites: Topics determine prerequisites.
• CSCI-GA.2945-​003
  MATH-GA 2012.001
  (3245) Advanced Topics in Numerical Analysis: Monte Carlo Methods  Jonathan Weare T 1:25-3:15PM CIWW 1302
  Topics vary each semester.
  
  Prerequisites: Topics determine prerequisites.
• CSCI-GA.2965-​001
  (3045) Heuristic Problem Solving  Dennis Shasha R 7:10-9:10PM CIWW 317
  This course revolves around several problems new to computer science (derived from games or puzzles in columns for Dr. Dobb’s Journal, Scientific American, and elsewhere). The idea is to train students to face a new problem, read relevant literature, and come up with a solution. The solution entails winning a contest against other solutions. The winner receives candy. The best solutions become part of an evolving “Omniheurist” Web site that is expected to get many visitors over the years. The course is for highly motivated, mathematically adept students. It is open to supported Ph.D. students and well-qualified master’s students. Class size has been around 10 in the past, and instructor and students have all gotten to know one another very well. Algorithmic and programming knowledge is the main prerequisite. It also helps to be familiar with a rapid prototyping language such as Matlab, Mathematica, K, or Python, or to be completely fluent in some other language.
  
  Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
• CSCI-GA.3033-​025
  (2355) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming  Mohamed Zahran W 4:55-6:55PM Blended
  

  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 how to program 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-​034
  (23234) Special Topics: Multicore Processors: Architecture & Programming  Mohamed Zahran T 4:55-6:55PM Blended
  

  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-​061
  (2849) Special Topics: Predictive Analytics  Anasse Bari W 4:55-6:55PM 60 Fifth Ave 110
  

  Predictive analytics is the art and science of extracting useful information from historical data and present data for the purpose of predicting future trends. In this course, students will have an introduction to the phases of the analytics lifecycle and a basic understanding of a variety of tools and machine learning techniques to analyze data to discover forward insights. Several techniques will be introduced; including data pre-processing techniques, data clustering algorithms, data classification algorithms, association rules data mining algorithms, recommender systems, etc.

  Applications from bio-informatics, social networks analytics, and text mining will be covered. Highlights from industrial use cases will be covered to demonstrate how Predictive Analytics relates to improving business performance and impacting better decisions. This is an introductory course that will provide students with basic skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.

• CSCI-GA.3033-​078
  (3548) Special Topics: Cryptocurrencies and Decentralized Ledgers  Joseph Bonneau T 7:10-9:10PM 60 Fifth Ave 150
  

  Bitcoin, Ethereum, and other systems using decentralized ledgers (blockchains) have quickly grown to valuations of hundreds of billions of dollars. Their future potential has captured the imagination of researchers working on applications as diverse as banking and finance, voting, law, corporate governance, gambling and online gaming. This course will cover the technical concepts underlying these systems: append-only ledgers, decentralized consensus, smart contracts and zero-knowledge proof systems. Students will gain working knowledge of both Bitcoin and Ethereum through practical assignments. The course will also survey the wide variety of potential future applications.

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java. An undergraduate course in cryptography or successful completion of CSCI-GA.3205 or CSCI-GA.3210 strongly recommended.
• CSCI-GA.3033-​083
  (3206) Special Topics: Machine Learning for Healthcare  Rajesh Ranganath M 7:10-9:10PM CIWW 101
  

  The course covers machine learning methods important for healthcare including causal inference, k-shot learning, time series models, and fairness. We will learn about important clinical questions and the data (such as from ICU monitors and imaging) that along with machine learning can help answer these questions. We will focus on recent work in machine learning for healthcare.

   
• CSCI-GA.3033-​085
  (2354) Special Topics: Cloud and Machine Learning  I-Hsin Chung
  Hao Yu R 4:55-6:55PM 60 Fifth Ave 110
  

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

   
• CSCI-GA.3033-​087
  (3229) Special Topics in CS: Bayesian Machine Learning  Andrew Gordon Wilson R 4:55-6:55PM 60 Fifth Ave C15
  

  This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will comprise three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference. 

  
  
  Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.
• CSCI-GA.3033-​091
  (3099) Special Topics: Introduction to Deep Learning Systems  Parijat Dube T 7:10-9:10PM Bobst LL138
  

  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.

   
• CSCI-GA.3033-​096
  (3276) Special Topics: Data Analytics and Visualization in Healthcare  Maria Lorena Carlo Unda R 7:10-9:10PM CIWW 101
  

  This course provides a foundational knowledge of data analytics and visualization applied to healthcare settings. Topics include Health Information Technology (HIT) systems and components, using R and Python programming languages for data analytics and visualization, best practices in data visualization, designing dashboards using Tableau, and descriptive and predictive analytics methods for healthcare.

   
• CSCI-GA.3033-​101
  (23322) Special Topics: Verification  Joseph Tassarotti W 4:55-6:55PM CIWW 312
  

  This course covers the formal verification of software using tools called proof assistants. Students will learn how to state a specification for a program's behavior and construct a machine-checked proof showing that a program satisfies its specification. Topics include dependent type theory, program logics, and reasoning about fault tolerance, concurrency, and randomized behaviors.

• CSCI-GA.3033-​102
  (23324) Special Topics: Learning with Large Language and Vision Models  Saining Xie M 4:55-6:55PM CIWW 312
  

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

  
  
  Prerequisites: Students must have completed courses in either deep learning, machine learning, or computer vision and demonstrate proficiency in Python programming.
• CSCI-GA.3033-​103
  (26238) Special Topics: Quantum Computing  Nicholas Spooner M 2:00-4:00PM CIWW 312
  

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

  
  
  Prerequisites: A strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), discrete mathematics (e.g. NYU's MATH-UA.0120 Discrete Mathematics) and discrete probability (e.g., NYU’s MATH-UA 235 Probability and Statistics). Familiarity with theory of computation (e.g., NYU’s CSCI-UA.0310 Basic Algorithms or CSCI-GA 1170 Fundamental Algorithms) is desirable. No background in physics is required.
• CSCI-GA.3033-​104
  (26239) Special Topics: Foundations of Deep Learning Theory  Matus Telgarsky W 2:00-4:00PM CIWW 312
  

  A rigorous mathematical introduction to the approximation, optimization, and generalization properties of deep neural networks. Topics include: constructive and non-constructive approximations with one hidden layer; benefits of depth; approximation properties of transformers; optimization in the NTK regime; maximum margin optimization outside the NTK regime; Rademacher complexity, VC dimension, and covering number bounds for ReLU networks. Evaluation is based on homeworks and a course project. The course goal is to prepare students to perform their own research in the field.

  
  
  Prerequisites: Probability, real analysis.
• CSCI-GA.3033-​183
  (3208) Special Topics: Machine Learning for Healthcare Recitation  Aahlad Puli F 1:30-2:20PM 60 Fifth Ave 110 Additional information: Recitation for CSCI-GA 3033-083
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​187 Special Topics in CS: Bayesian Machine Learning Lab  Fred Lu F 11:15-12:05PM Bobst LL141
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​925
  (23492) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Recitation  Ta-Hsien Hsu F 11:15-12:05PM CIWW 101 Additional information: Recitation for CSCI-GA 3033-025
  

  This is the recitation for section 025. Refer to the lecture for description.

• CSCI-GA.3033-​934
  (23493) Special Topics: Multicore Processors: Architecture & Programming Recitation  Amitkumar Patel F 12:30-1:20PM CIWW 101 Additional information: Recitation for CSCI-GA 3033-034
  

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

• CSCI-GA.3110-​001
  (3046) Honors Programming Languages  Patrick Cousot TR 3:30-4:45PM 60 Fifth Ave 261
  The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.
  
  Prerequisites: Permission of the instructor for master’s students
• CSCI-GA.3205-​001
  (2938) Applied Cryptography & Network Security  Mazdak Zamani T 7:10-9:10PM 60 Fifth Ave 110
  This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
• CSCI-GA.3210-​001
  MATH-GA 2170-001
  (23240) Introduction To Cryptography  Marshall Ball R 7:10-9:10PM CIWW 312
  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.3520-​001
  (2356) Honors Analysis of Algorithms  Subhash Khot TR 9:30-10:45AM 19W4 102
  Design of algorithms and data structures. Review of searching, sorting, and fundamental graph algorithms. In-depth analysis of algorithmic complexity, including advanced topics on recurrence equations and NP-complete problems. Advanced topics on lower bounds, randomized algorithms, amortized algorithms, and data structure design as applied to union-find, pattern matching, polynomial arithmetic, network flow, and matching.
  
  Prerequisites: Permission of the instructor for master’s students.
• CSCI-GA.3812-​001
  (2357) Information Technology Projects *  Jean-Claude Franchitti M 6:10-9:10PM GCASL 269
  

  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
  (2358) 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
  (2359) 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
  (2360) 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
  (2361) 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
  (2362) 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-​006
  (2363) 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-​007
  (2364) 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-​008
  (2365) 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.3840-​001
  (2366) 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
  (2990) PhD Seminar: Cryptography**  Yevgeniy Dodis T 12:00-2:00PM 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
  (2367) PhD Seminar: Formal Methods**  Joseph Tassarotti R 11:00-12:30PM 60 Fifth Ave 446
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-GA.3860-​001
  (2368) PhD Thesis Research **  STAFF -
  
  
  Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
• CSCI-GA.3870-​001
  (2865) 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
  (2866) 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.

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

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