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-​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.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.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.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-​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-​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-UA.0102-​011
  (11124) Data Structures  CANCELLED F 8:00-9:15AM Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​012
  (11126) Data Structures  TBA F 9:30-10:45AM CIWW 312 Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​013
  (11128) Data Structures  TBA F 11:00-12:15PM CIWW 312 Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​014
  (11130) Data Structures  CANCELLED F 12:30-1:45PM Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​015
  (11132) Data Structures  TBA F 2:00-3:15PM CIWW 312 Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​016
  (11134) Data Structures  CANCELLED F 3:30-4:45PM Additional information: In-person recitation for Section 10
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​021
  (11138) Data Structures  TBA F 11:00-12:15PM 194M 208 Additional information: In-person recitation for Section 20
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​022
  (11140) Data Structures  TBA F 2:00-3:15PM GCASL 279 Additional information: In-person recitation for Section 20
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​031
  (11144) Data Structures  TBA F 9:30-10:45AM CIWW 317 Additional information: In-person recitation for Section 30
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​032
  (11146) Data Structures  TBA F 11:00-12:15PM CIWW 317 Additional information: In-person recitation for Section 30
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​033
  (11148) Data Structures  TBA F 12:30-1:45PM CIWW 317 Additional information: In-person recitation for Section 30
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​041
  (11152) Data Structures - Recitation  STAFF F 9:30-10:45AM Bobst LL138 Additional information: In-person recitation for Section 40
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​051
  (11156) Data Structures  STAFF F 2:00-3:15PM GCASL 388 Additional information: In-person recitation for Section 50
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​061
  (11160) Data Structures  TBA F 12:30-1:45PM Tisch LC11 Additional information: In-person recitation for Section 60
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​071
  (11164) Data Structures  TBA F 11:00-12:15PM Silv 403 Additional information: In-person recitation for Section 70
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0201-​002
  (11168) Computer Systems Organization - Recitation  TBA F 9:30-10:45AM 60 Fifth Ave 150 Additional information: In-person recitation for Section 01
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​004
  (11950) Computer Systems Organization - Recitation  TBA F 9:30-10:45AM Online Additional information: Online recitation for Section 03
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​006
  (11174) Computer Systems Organization - Recitation  TBA F 11:00-12:15PM Silv 411 Additional information: In-person recitation for Section 05
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​008
  (11178) Computer Systems Organization - Recitation  TBA F 11:00-12:15PM Silv 207 Additional information: In-person recitation for Section 07
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0310-​002
  (11182) Basic Algorithms - Recitation  TBA F 9:30-10:45AM 194M 304 Additional information: In-person recitation for Section 01
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​004
  (11185) Basic Algorithms - Recitation  Alan Siegel F 2:00-3:15PM Online Additional information: Online recitation for Section 03
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​006
  (11189) Basic Algorithms - Recitation  TBA F 12:30-1:45PM Silv 208 Additional information: In-person recitation for Section 05
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​008
  (11193) Basic Algorithms - Recitation  STAFF F 8:00-9:15AM GCASL 261 Additional information: In-person recitation for Section 07
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​010
  (11197) Basic Algorithms - Recitation  STAFF F 9:30-10:45AM Bobst LL150 Additional information: In-person recitation for Section 09
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​021
  (11199) Basic Algorithms - Recitation  Wilson Nguyen F 11:00-12:15PM 12WV L120 Additional information: In-person recitation for Section 01
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​022
  (11201) Basic Algorithms - Recitation  Wilson Nguyen F 2:00-3:15PM Tisch LC11 Additional information: In-person recitation for Section 01
  

  Course Description:

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0330-​002
  (11223) Introduction to Computer Simulation  Claire Nicole Valva F 11:00-12:15PM CIWW 201 Additional information: In-person recitation for Section 01
  

  Course Description:

  In this course, students will learn how to do computer simulations of such phenomena as orbits (Kepler problem and N-body problem), epidemic and endemic disease (including evolution in response to the selective pressure of a malaria), musical stringed instruments (piano, guitar, and violin), and traffic flow in a city (with lights, breakdowns, and gridlock at corners). The simulations are based on mathematical models, numerical methods, and Matlab programming techniques that will be taught in class. The use of animations (and sound where appropriate) to present the results of simulations will be emphasized.

  
  
  Prerequisites: Calculus I (MATH-UA 121) or Math for Economics II (MATH-UA 212) and General Physics (PHYS-UA 11).
• CSCI-UA.0473-​002
  (11225) Fundamentals of Machine Learning - Recitation  TBA F 9:30-10:45AM Silv 405 Additional information: In-person recitation for Section 01
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0473-​021
  (11957) Fundamentals of Machine Learning - Recitation  TBA F 2:00-3:15PM Silv 405 Additional information: In-person recitation for Section 01
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).

• *
  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