Course Schedule

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

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• 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-​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-​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.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.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-​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-​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.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.

• *
  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