Course Schedule

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

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• CSCI-GA.1170-​002
  DS-GA.1170-002
  (8919) Fundamental Algorithms Recitation  Daniel Collins M 5:55-6:45PM 5WP 101 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.2110-​002
  (8925) Programming Languages Recitation  Elaine Li M 4:55-5:45PM 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
  (8926) 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.2250-​001
  (8929) Operating Systems  Hubertus Franke M 7:10-9:10PM CIWW 109
  

  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.2420-​001
  MATH-GA 2010-001
  (15848) Numerical Methods I   Benjamin Peherstorfer MW 4:55-6:10PM Silv 401
  

  Course Description:

  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.2560-​001
  (8936) Artificial Intelligence   Paul Bethe M 4:55-6:55PM CIWW 101
  

  Course Description:

  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.2572-​002
  DS-GA.1008-002
  (8999) Deep Learning Lab  Peter Tong M 3:45-4:35PM 5WP 101 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.2840-​001
  (8983) Entrepreneurship Capstone  Evan Korth M 10:15-12:15PM 194M 306A
  

  Course Description:

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

  The Entrepreneurship Capstone class is a 15­week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.

• CSCI-GA.2840-​002
  (8984) Entrepreneurship Capstone Lab  Charlie O'Donnell M 12:30-1:20PM 194M 305 Additional information: Lab for Section 001
  

  Course Description:

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

  The Entrepreneurship Capstone class is a 15­week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.

• CSCI-GA.3033-​078
  (20497) Special Topics: Cryptocurrencies and Decentralized Ledgers  Joseph Bonneau M 7:10-9:10PM 60 Fifth Ave C15
  

  Course Description:

  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-​086
  (8970) Special Topics: Quantum Computation  Oded Regev M 2:45-4:45PM CIWW 317
  

  Course Description:

  This course will provide an introduction to quantum computation and information. We will cover topics such as the quantum circuit model of computation, the Fourier transform, quantum algorithms like Deutsch-Jozsa, Simon, and Grover, Shor's factoring algorithm and discrete log. Topics in quantum information will also be discussed, such as quantum entanglement, teleportation, superdense coding, and quantum nonlocal games. We will also mention quantum cryptography and quantum key distribution. The course will be of interest to students working in computer science, mathematics, or physics.
  
  Prerequisites include a strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), and some familiarity with 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) is a plus. No background in physics is required. Evaluation will be based on class participation, homework, and exams.

  
  
  Prerequisites: Prerequisites include a strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), and some familiarity with 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) is a plus. No background in physics is required. Evaluation will be based on class participation, homework, and exams.
• CSCI-GA.3033-​087
  DS-GA.3001-011
  (20474) Special Topics: Bayesian Machine Learning  Andrew Gordon Wilson M 10:15-12:15AM GCASL 275
  

  Course Description:

  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-​121
  (8969) Special Topics: Programming Parallel Algorithms  Sam Westrick M 10:15-12:15PM 60 Fifth Ave C10
  

  Course Description:

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

  
  
  Prerequisites: CSCI-GA.2110-001 Programming Languages. Students should be comfortable with recursive programming techniques and familiar with basic data structures and algorithms.
• CSCI-GA.3033-​125
  (20479) Special Topics: Reinforcement Learning with Foundation Models  Sherry Yang M 2:45-4:45PM 60 Fifth Ave 110
  

  Course Description:

  Reinforcement learning has traditionally focused on task-specific, tabula rasa settings with limited prior knowledge. Recent research in applying RL on top of foundation models pretrained on internet-scale data has led to LLMs capable of solving a variety of reasoning, coding, and math problems. RL algorithms combined with video world models have also led to fruitful research outcomes in embodied learning. In this course, we will study these RL algorithms and their practical implications when applied to align foundation models in task-specific downstream settings.

  This course is designed to be a PhD-level research course.  Students taking this course should have successfully completed CSCI-GA.3033-090 Deep Decision Making & Reinforcement Learning usually taught in the spring by Professor Lerrel Pinto.  Students enrolled in this class should also be interested in pursuing conference-style publications in Reinforcement Learning.

  
  
  Prerequisites: Linear algebra, differential equations, CSCI-GA.3033-090 Deep Decision Making & Reinforcement Learning.
• CSCI-GA.3033-​7863
  ROB-GY.7863-A
  (23168) Special Topics: Space Robotics  Benjamin Riviere M 2:00-4:30PM Jacobs 211
  

  Course Description:

  This class will study the math and implementation of planning, learning, and control algorithms through applications in spacecraft, rocket landings, and rover autonomy. We will cover classical topics and emerging research areas in linear and nonlinear dynamics, optimal control, numerical optimization, Bayesian estimation, search-based planning, and reinforcement learning. We will study existing applications like precision rocket landings and martian rover path planning, and proposed future missions like distributed apertures of spacecraft swarms and complex locomotion for planetary exploration.

   

  The goals of the course are: (i) to build fundamental understanding of robot planning, learning, and control, (ii) to introduce applications in aerospace robotics and (iii) to prepare students to become researchers and devise novel methods and algorithms. The grading will be coding-assignments and project-based. This class is targeted towards graduate-level students with familiarity in robotics, linear algebra, differential equations, probability, and Python programming. If you are unsure, feel free to contact Dr. Riviere at riviere.b@nyu.edu.
• CSCI-GA.3812-​001
  (8974) Information Technology Projects *  Jean-Claude Franchitti M 6:10-9:10PM 60 Fifth Ave 110
  

  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.

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Classroom Locations