Course Schedule

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

View undergraduate course grid View graduate course grid

• 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.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.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.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.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.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-​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-​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-​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-​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.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.

• *
  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