Course Schedule

Current semester Spring 2026, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-001
DS-GA.1170-001
(7413) Fundamental Algorithms Chee Yap W 4:55-6:55PM CIWW 109

Course Description:

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-002
DS-GA.1170-002
(7414) Fundamental Algorithms Recitation Bingwei Zhang R 5:55-6:45PM Meyr 122 Additional information: Recitation for Section 001

Course Description:

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.1170-002
(7415) Fundamental Algorithms Recitation Bingwei Zhang F 4:55-5:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-004
BMIN-GA.1170-001
(7416) Fundamental Algorithms Yevgeniy Dodis T 4:55-6:55PM CIWW 312

Course Description:

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-005
(7417) Fundamental Algorithms Recitation Daniel Collins R 3:45-4:35PM 12WV L113 Additional information: Recitation for Section 004

Course Description:

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
BMIN-GA.1180-001
(7418) Mathematical Techniques For CS Applications Parijat Dube R 7:10-9:10PM CIWW 101

Course Description:

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
(7419) Programming Languages Cory Plock M 4:55-6:55PM CIWW 109

Course Description:

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
(7420) Programming Languages Recitation Alexander Bai R 7:10-8:00PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

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
(7421) Programming Languages Recitation Zichen Zhang F 11:15-12:05PM Silv 408 Additional information: Recitation for Section 001

Course Description:

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
(7422) Compiler Construction Sam Westrick M 10:15-12:15PM GCASL 388

Course Description:
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
(7423) Operating Systems Hubertus Franke M 2:45-4:45PM GCASL 383

Course Description:

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2250-002
(7424) Operating Systems Mohamed Zahran T 4:55-6:55PM Online

Course Description:

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2262-001
(7425) Data Communications and Networks Jean-Claude Franchitti M 7:10-9:10PM 60 Fifth Ave 150

Course Description:

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

Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
CSCI-GA.2421-001
MATH-GA 2020-001
(12950) Numerical Methods II Michael O'Neil MW 11:00-12:15PM CIWW 1302

Course Description:

This course will cover fundamental methods that are essential for the numerical solution of differential equations. It is intended for students familiar with ODE and PDE and interested in numerical computing; computer programming assignments form an essential part of the course. The course will introduce students to numerical methods for (1) nonlinear equations, Newton's method; (2) ordinary differential equations, Runge-Kutta and multistep methods, convergence and stability; (3) finite difference, finite element and integral equation methods for elliptic partial differential equations; (4) fast solvers, multigrid methods; and (5) parabolic and hyperbolic partial differential equations.

Text: LeVeque, R. (2007). Classics in Applied Mathematics [Series]. Finite Difference Methods for Ordinary and Partial Differential Equations. Philadelphia, PA: Society for Industrial and Applied Mathematics

Prerequisites: Numerical linear algebra, elements of ODE and PDE.
CSCI-GA.2433-001
DS-GA.2433-001
(7426) Database Systems Matthew Zeidenberg W 4:55-6:55PM 60 Fifth Ave 150

Course Description:
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
BMIN-GA.2436-001
(7470) Realtime and Big Data Analytics Yang Tang R 4:55-6:55PM CIWW 101

Course Description:

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
(7471) Big Data Application Development Yang Tang T 7:10-9:10PM CIWW 101

Course Description:

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: Students are required to successfully complete CSCI-GA 2436 Realtime and Big Data Analytics prior to taking this course. Students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.2565-001
(7428) Machine Learning Rajesh Ranganath W 7:10-9:10PM 60 Fifth Ave 150

Course Description:

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

Prerequisites: Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics.
CSCI-GA.2572-001
DS-GA.1008-001
(23239) Deep Learning Alfredo Canziani
Yann LeCun M 4:55-6:55PM 60 Fifth Ave 150

Course Description:

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

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2572-002
DS-GA.1008-002
(23243) Deep Learning Lab Alfredo Canziani
Yann LeCun F 12:30-1:20PM Silv 207 Additional information: Lab for Section 001

Course Description:

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
BMIN-GA 2590-001
(7429) Natural Language Processing Eunsol Choi W 4:55-6:55PM Silv 207

Course Description:

How can we teach machines to understand human languages so that they can answer queries, summarize dense information, or hold a conversation with us? The primary goal of this course is to provide students with the principles and state-of-the-art tools needed to solve a variety of NLP problems. We will focus on three paradigms in the field of NLP: supervised learning, pretrain-then-finetune, and the most recent large language models, with a focus on representation learning techniques. Students are expected to read research papers and gain hands-on experience through coding assignments.

Prerequisites:
Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235); Algorithms and data structure (CSCI-UA.102); Basic knowledge in machine learning (DS-GA.1003, CSCI-UA.0473). We will not spend a significant amount of time on machine learning basics so some prior exposure to the supervised learning framework (e.g., loss functions, SGD) is expected. Good comfort level with Python
CSCI-GA.2620-001
(7430) Networks and Mobile Systems Lakshminarayanan Subramanian T 10:15-12:15PM GCASL 275

Course Description:

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

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2621-001
(7468) Distributed Systems Aurojit Panda W 4:55-6:55PM CIWW 101

Course Description:

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

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

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

Prerequisites: 1. Undergraduate Operating Systems or Computer Systems Organization; 2. programming experience; 3. mathematical maturity or the willingness to work through simple proofs.
CSCI-GA.2820-001
(7461) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program

Course Description:

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
(7462) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001. Open only to students in the Stern TECH MBA program.

Course Description:

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
(7463) DevOps and Agile Methodologies John Rofrano W 4:55-6:55PM Online

Course Description:

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
(7464) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM Online Additional information: Lab for Section 003

Course Description:

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
(7465) The Lean Launch Pad* Yiannis Broustas R 10:15-12:15PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program.

Course Description:

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
(7466) The Lean Launch Pad Lab* Yiannis Broustas R 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program.

Course Description:

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.2945-004
MATH-GA 2012-002
(17409) Adv Topics Num Analysis: Convex & Non-Smooth Operation Michael Overton TR 2:00-3:15PM CIWW 1302

Course Description:

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.3033-026
(7447) Special Topics: Cloud Computing Jean-Claude Franchitti R 7:10-9:10PM CIWW 312

Course Description:

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

Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
CSCI-GA.3033-034
(7448) Special Topics: Multicore Processors: Architecture & Programming Mohamed Zahran W 12:30-2:30PM Online

Course Description:

This course serves as a capstone for the MSCS program.

The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable.
CSCI-GA.3033-059
BMIN-GA.3033-005
(7449) Special Topics: Big Data Science Anasse Bari W 4:55-6:55PM CIWW 317

Course Description:

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.3033-076
(7450) Special Topics: Vision Meets Machine Learning Davi Geiger W 10:15-12:15PM Tisch LC11

Course Description:

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.3033-085
(7452) Special Topics: Cloud and Machine Learning CANCELLED -

Course Description:

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

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
CSCI-GA.3033-097
(7453) Special Topics: Virtual Reality* Kenneth Perlin T 4:55-6:55PM 60 Fifth Ave 446

Course Description:

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

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

Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
CSCI-GA.3033-099
DS-GA 3001-009
(23398) Special Topics in DS: Introduction to Computer Vision Jean Ponce M 7:10-9:10PM 19W4 102

Course Description:

This course aims to cover broad topics in computer vision, and is not primarily a deep learning course. We will cover topics in traditional computer vision such as camera geometry, image formation, segmentation, object recognition, classification, and detection.
CSCI-GA.3033-102
(7454) Special Topics: Learning with Large Language and Vision Models CANCELLED -

Course Description:

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.3033-107
(7455) Special Topics: Cryptography of Blockchains Benedikt Bünz M 12:30-2:30PM 60 Fifth Ave C10

Course Description:

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

Prerequisites: Strong mathematical background, Introductory cryptography (CSCI-GA.3210-001) or similar. No knowledge of blockchains is required. The course is mostly self-contained. Contact instructor if unsure.
CSCI-GA.3033-109
(7456) Special Topics: Computer Vision for Science and Engineering David Fouhey T 4:55-6:55PM CIWW 101

Course Description:

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

Prerequisites: Students should come with familiarity with computer vision or machine learning as well as an enthusiasm for trying new data but no formal prerequisites are required.
CSCI-GA.3033-115
DS-GA.3001-003
(8835) Special Topics: Embodied Learning and Vision Mengye Ren T 2:45-4:45PM 12WV L120

Course Description:

This graduate level course covers advanced topics in embodied visual learning, perception and planning, with applications to robotics and self-driving. Topics include deep learning, computer vision, 3D perception, unsupervised 3D learning, self-supervised representation learning, continual learning, and LLM agents. Students are expected to know basic knowledge of machine learning, deep learning, and computer vision. Students are expected to be proficient in programming in Python. Advanced undergraduate students may enroll upon instructor approval.
CSCI-GA.3033-118
(7457) Special Topics: Complexity Subhash Khot T 12:30-2:30PM CIWW 202

Course Description:

The first part of the course will cover basic aspects of complexity theory. This includes complexity classes P, NP, L, NL, PSPACE, Polynomial Hierarchy, BPP, P/poly, NC, IP, AM, #P and relationships among them.

The second part of the course will cover advanced toipcs, e.g. PCPs, circuit lower bounds, communication complexity, derandomization, property testing and quantum computation. The emphasis will be on breadth rather than covering any of these topics in depth. We will introduce a specific topic, give a broad overview of results, and only a glimpse of the techniques. During this part of the course, you would need to read a couple of advanced research papers/surveys and write a report.
CSCI-GA.3033-119
DS-GA.3001-013
(19701) Special Topics: Generative Models and Decision Making Rajesh Ranganath T 7:10-9:10PM 60 Fifth Ave 125

Course Description:

This class will cover advanced topics in generative models along with their use in decision-making. Topics will include autoregressive language models, diffusion/flows, steering, causal inference, and reinforcement learning. The class will run as a seminar with a focus on discussing and analyzing recent research papers. The class requires background knowledge in machine learning. Advanced undergraduates are encouraged to join.

Prerequisites: Previous coursework in probability and machine learning/AI
CSCI-GA.3033-123
(17162) Special Topics: Encrypted Computation Nir Bitansky W 12:30-2:30PM 60 Fifth Ave C15

Course Description:

The modern computing environment is very different than it used to be. Sensitive information that once existed only on local trusted machines—medical records, personal preferences, and our finances–is now stored and processed by various entities onew the web, who we often do not trust. To maintain privacy in this reality, a new array of cryptographic tools has been developed over the past decade and a half allowing different forms of encrypted computation. This includes: fully-homomorphic encryption, attribute-based encryption, functional encryption, and program obfuscation. The course will cover some of these advances with special focus on the main open questions in the area.
CSCI-GA.3033-129
BMIN-GA.4551-001
(20759) Special Topics: Artificial Intelligence in Genomics Nadav Brandes R 12:00-2:30PM MSB 390CR

Course Description:

The course explores the practical applications of AI in genomics and genetics. Students will learn how AI can integrate diverse biological data – from evolutionary patterns across species to clinical records – to interpret our genome and help diagnose medical conditions, predict disease risk, and engineer new therapeutic proteins. A major focus of the course will be hands on work with modern AI techniques that address real world applications. This includes training models from scratch, as well as applying and fine-tuning existing models. Key machine-learning and AI concepts will be thoroughly covered, but we will largely avoid the mathematical theory taught in other machine learning courses. Our emphasis will be on implementation of these powerful models to advance biomedicine.

AI in Genomics for the Spring 2026 semester is scheduled to be in the Medical Science Building 3rd Floor: 3-90 CR with the exception of the following dates:

Feb 5, 2026: MSB 3-101 CR.

Feb 12, 2026: MSB 3-101 CR.

Feb 19, 2026: MSB 3-101 CR.

Mar 12, 2026: MSB 3-101 CR.

Apr 9, 2026: MSB 3-101 CR.
CSCI-GA.3033-130
CS-GY.9223-J
(21442) Special Topics: Randomness and Computation Sanjeev Khanna T 2:45-4:45PM CIWW 202

Course Description:

Can you determine whether two very large documents are identical by examining only a handful of bits? Can you verify the output of a program without redoing the entire computation? Can you be convinced of the correctness of a mathematical proof by checking only a few random bits? Can you properly color a graph without ever seeing all of its edges? Surprisingly, the answer to all these questions is yes — provided we allow the use of randomization.

Randomness has emerged as a fundamental resource in computation, enabling algorithms that are often simpler, faster, or more space-efficient than their deterministic counterparts. This course will explore the design and analysis of randomized algorithms, as well as their inherent limitations, studied through query complexity and communication complexity. Topics include sublinear algorithms, approximation algorithms and hardness of approximation, algebraic fingerprinting techniques, interactive proofs and the PCP theorem, random walks and Markov chains with applications to bounded-space computation, and the complexity classes that capture randomized computation, including BPP, RP, and ZPP.

The course is suitable for graduate students and advanced undergraduates, with a strong preparation in undergraduate algorithms (for instance, CSCI-GA.3520-001 or equivalent).

Note: The topics in this course are largely disjoint from those in Prof. Anupam Gupta’s Fall 2025 class on Randomized Algorithms. This course is suitable both for students who have not taken Prof. Gupta’s class and for those who have, and wish to continue exploring randomized algorithms.
CSCI-GA.3033-131
DS-GA.3001-009
(22480) Special Topics: Building LLM Reasoners Greg Durrett F 12:30-2:30PM CIWW 101

Course Description:

Large language models are a foundational building block of generative AI systems. Modern LLMs go far beyond the capabilities of early LLMs like GPT-3 by demonstrating the capacity for reasoning in natural language. This course provides a hands-on crash course into what makes these models work, covering (1) fundamental building blocks like tokenizers and optimizers; (2) optimizations such as FlashAttention; (3) approaches for learning reasoners such as supervised fine-tuning, reinforcement learning, and inference-time methods. Assignments will focus on implementation of these approaches.

Prerequisites: CSCI-GA.2590 Natural Language Processing or equivalent; CSCI-GA-2565 Machine Learning or equivalent; Deep learning or equivalent; Python programming experience; Experience with concepts from probability, linear algebra
CSCI-GA.3033-132
MATH-GA.2011-001
(22484) Special Topics: Iterative Methods for Systems of Equations Florian Schaefer M 4:55-6:55PM CIWW 317

Course Description:

This class covers a wide range of iterative methods for solving linear and nonlinear systems of equations. Among the topics covered are fixed-point iterations, splitting methods, Krylov subspace methods, (multigrid) preconditioners, and Newton-type methods. The class will present the mathematical underpinnings of these methods and use them, together with numerical experimentation, to study their properties.

Prerequisites: Numerical Methods I is preferred.
CSCI-GA.3033-900
DS-GA.3001-014
(19702) Special Topics: Generative Models and Decision Making Recitation STAFF F 1:30-2:20PM 60 Fifth Ave 261 Additional information: Recitation for Section 119

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-934
(7458) Special Topics: Multicore Processors: Architecture & Programming Recitation Zephyr Luo F 12:30-1:20PM Silv 414 Additional information: Recitation for Section 034

Course Description:

This is the recitation for section 034. Refer to the lecture for description.
CSCI-GA.3033-998
DS-GA.3001-010
(7459) Special Topics: Embodied Learning and Vision (Recitation) STAFF T 4:55-5:45PM 12WV L113 Additional information: Recitation for Section 115

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-999
(23399) Special Topics: Intro to Computer Vision Recitation Krittin Nagar R 4:55-5:45PM GCASL 261

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-9483
ECE-GY.9483
(22714) Special Topics: Efficient AI and Hardware Accelerator Design Sai Qian Zhang F 5:00-7:30PM Jacobs 475

Course Description:

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

Prerequisites: This course is open to both PhD and MS students. Prerequisites include prior knowledge of computer architecture (CSCI-UA.0201-001 or equivalent) and basic understanding of deep learning (CSCI-GA.2572-001 or equivalent).
CSCI-GA.3140-001
(7469) Abstract Interpretation Patrick Cousot M 4:55-6:55PM 60 Fifth Ave 202

Course Description:

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
(7432) Applied Cryptography & Network Security Mazdak Zamani W 4:55-6:55PM GCASL 261

Course Description:
This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
CSCI-GA.3812-001
(7460) Information Technology Projects * Evan Korth W 5:00-8:00PM CIWW 312

Course Description:

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
(7433) Adv Lab MS * STAFF -

Course Description:

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
(7434) Adv Lab MS * STAFF -

Course Description:

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
(7435) Adv Lab MS * STAFF -

Course Description:

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
(7436) Adv Lab MS * STAFF -

Course Description:

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
(7437) Adv Lab PhD ** STAFF -

Course Description:

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
(7438) Adv Lab PhD ** STAFF -

Course Description:

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
(7439) Adv Lab PhD ** STAFF -

Course Description:

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
(7440) Adv Lab PhD ** STAFF -

Course Description:

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
(7441) Master's Thesis Research * STAFF -

Course Description:

Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
CSCI-GA.3850-001
(7442) PhD Seminar: Cryptography ** Yevgeniy Dodis T 11:00-1:50PM CIWW 317

Course Description:
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.3850-002
(17543) PhD Seminar: Theory Anupam Gupta R 11:00-1:50PM CIWW 317

Course Description:
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
(7443) PhD Seminar: Formal Methods Sam Westrick W 11:00-12:30PM 60 Fifth Ave 446

Course Description:
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
(7444) PhD Thesis Research ** STAFF -

Course Description:

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(7445) Internship In Computer Science MS* STAFF -

Course Description:

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
(7446) Internship in Computer Science PhD* STAFF -

Course Description:

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.

Classroom Locations

Notice: This webpage is subject to change and will be revised and updated as necessary.