Course Schedule

Current semester Spring 2018, and future semesters are now available to view.

View undergraduate course grid View graduate course grid

Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1122-001
(19883) Web Development**** Joshua Clayton
Office Hours T 6:20-9:00PM 60 Fifth Ave C04

Course Description:

This course uses a project-based learning approach towards the study of web technologies and web programming. Students study the principles of web design and each student builds one or more interactive websites based on content relevant to their scholarly interest in the humanities or social sciences. Not open to graduate Computer Science, Information Systems, Mathematics or Scientific Computing students. For registration, please contact dhss@nyu.edu.
CSCI-GA.1144-001
(2313) PAC II* Mohamed Zahran
Office Hours T 6:00-8:30PM CIWW 312

Course Description:

This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems

Prerequisites:
CSCI-GA 1133 or departmental permission.
CSCI-GA.1144-002
(2744) PAC II Recitation Srinidhi Goud Myadaboyina
Office Hours R 7:10-8:00PM CIWW 312

Course Description:

This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems

Prerequisites:
CSCI-GA 1133 or departmental permission.
CSCI-GA.1170-001
DS-GA.1170
(2314) Fundamental Algorithms Joel Spencer
Office Hours M 5:10-7:00PM Silv 405

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
(2315) Fundamental Algorithms Recitation Ojas Deshpande R 8:10-9:00PM 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.1170-003
DS-GA.1170
(3347) Fundamental Algorithms Alexander Alekseyev
Office Hours T 7:10-9:00PM 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.1170-004
DS-GA.1170
(19881) Fundamental Algorithms Recitation Puneet Kaur
Office Hours W 5:10-6:00PM CIWW 101

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.2110-001
(2316) Programming Languages Benjamin Goldberg
Office Hours W 7:10-9:00PM 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
(2317) Programming Languages Recitation Chirag Maheshwari R 7:10-8:00PM 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.2130-001
(2318) Compiler Construction Eva Rose
Office Hours M 7:10-9:00PM CIWW 202

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
(2319) Operating Systems Hubertus Franke
Office Hours M 7:10-9:00PM 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.2250-002
(3014) Operating Systems Allan Gottlieb
Office Hours T 5:10-7:00PM 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.2262-001
(2320) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 5:10-7:00PM CIWW 101

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.2270-001
(19885) Computer Graphics Jeremie Dumas
Office Hours
Daniele Panozzo W 7:10-9:00PM CIWW 312

Course Description:

Problems and objectives of computer graphics. Vector, curve, and character generation. Interactive display devices. Construction of hierarchical image list. Graphic data structures and graphics languages. Hidden-line problems; windowing, shading, and perspective projection. Curved surface generation display.

Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
CSCI-GA.2421-001
MATH-GA 2020-001
(2321) Numerical Methods II Jonathan Goodman
Office Hours T 5:10-7:00PM CIWW 202

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.2433-001
DS-GA.2433-001
(2322) Database Systems Zvi Kedem
Office Hours W 5:10-7:00PM CIWW 109

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.2440-001
(2323) Software Engineering Jean-Claude Franchitti
Office Hours M 7:10-9:00PM CIWW 312

Course Description:
This is a capstone course focusing on large-scale software development. This course presents modern software engineering techniques and examines the software life cycle, including software specification, design, implementation, testing, and maintenance. Object-oriented design methods are also considered. Software engineering projects involve creation of a large-scale software system and require some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250
CSCI-GA.2520-001
BIOL-GA 1127-001
(2324) Bioinformatics and Genomes Brian Parker
Office Hours F 1:00-3:45PM Silv 411

Course Description:
The recent explosion in the availability of genome-wide data such as whole genome sequences and microarray data led to a vast increase in bioinformatics research and tool development. Bioinformatics is becoming a cornerstone for modern biology, especially in fields such as genomics. It is thus crucial to understand the basic ideas and to learn fundamental bioinformatics techniques. The emphasis of this course is on developing not only an understanding of existing tools but also the programming and statistics skills that allow students to solve new problems in a creative way.
CSCI-GA.2560-001
(19884) Artificial Intelligence Ernest Davis
Office Hours M 5:10-7:00PM CIWW 102

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.2567-001
DS-GA 1003-001
(23861) Machine Learning David Rosenberg
Office Hours T 5:20-7:00PM GCASL C95

Course Description:

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

Cross-listing for DS-GA 1003.

Prerequisites: DS-GA-1001: Intro to Data Science or its equivalent, DS-GA-1002: Statistical and Mathematical Methods or its equivalent. Solid mathematical background, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate calculus (primarily differential calculus), probability theory, and statistics. (The coverage in DS-GA 1002 is sufficient.) Python programming required for most homework assignments. Recommended: Computer science background up to a "data structures and algorithms" course. Recommended: At least one advanced, proof-based mathematics course
CSCI-GA.2567-002
DS-GA 1003-002
(23862) Machine Learning Lab Benjamin Jakubowski W 6:45-7:35PM Meyr 121

Course Description:

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

Cross-listing for DS-GA 1003.

Prerequisites: DS-GA-1001: Intro to Data Science or its equivalent, DS-GA-1002: Statistical and Mathematical Methods or its equivalent. Solid mathematical background, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate calculus (primarily differential calculus), probability theory, and statistics. (The coverage in DS-GA 1002 is sufficient.) Python programming required for most homework assignments. Recommended: Computer science background up to a "data structures and algorithms" course. Recommended: At least one advanced, proof-based mathematics course
CSCI-GA.2572-001
DS-GA.1008-001
(23867) Deep Learning Yann LeCun M 4:55-6:35PM CIWW 109

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
(23868) Deep Learning Lab Alfredo Canziani
Mikael Henaff T 9:10-10:00PM CIWW 109

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
(2745) Natural Language Processing Ralph Grishman
Office Hours T 5:10-7:00PM 60 Fifth Ave 110

Course Description:

Natural Language Processing applies computational and linguistic knowledge to the processing of natural languages (English, Chinese, Spanish, Japanese). Applications include: machine translation, information extraction, information retrieval, and others. On the one hand, the class will include the modeling and representation of linguistic phenomena. On the other, It will cover methods for applying this knowledge using both manual rules and machine learning. Sample topics include: formal languages, hmm, part of speech tagging, vector-based methods, shallow and full parsing, semantic role labeling, information extraction and machine translation. Students will complete shared tasks (POS-tagging, Information Extraction, etc.) and group final projects. There will also be a midterm exam and several small written assignments.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
CSCI-GA.2620-001
(2784) Networks and Mobile Systems Anirudh Sivaraman
Office Hours R 7:10-9:00PM 60 Fifth Ave C10

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.2945-001
MATH-GA.2012-001
(2325) Adv Topics Num. Analysis: Convex and Nonsmooth Optimization Michael Overton
Office Hours M 5:10-7:00PM CIWW 1302

Course Description:
Topics vary each semester.

Prerequisites: Topics determine prerequisites.
CSCI-GA.2946-001
DS-GA.3001-010
(23908) Mathematics of Data Science: Graphs and Networks Afonso Bandeira
Office Hours R 4:55-6:35PM 60 Fifth Ave 110

Course Description:

Topics vary each semester.
CSCI-GA.2946-002
DS-GA.3001-011
(23909) Mathematics of Data Science: Graphs and Networks Lab Shuyang Ling
Office Hours W 7:45-8:35PM 60 Fifth Ave 110

Course Description:

Topics vary each semester.
CSCI-GA.3033-001
(2327) Special Topics: Big Data Science Anasse Bari
Office Hours W 5:10-7:00PM CIWW 201

Course Description:

Topics vary each semester.

Prerequisites: Strong programming skills in the Java language and fundamentals of linear algebra.
CSCI-GA.3033-002
(2835) Special Topics: Social Networks Bhubaneswar Mishra
Office Hours T 5:10-7:00PM CIWW 201

Course Description:

Topics vary each semester.

Prerequisites: Discrete math and linear algebra
CSCI-GA.3033-003
(2328) Special Topics: Production Quality Software Michael Schidlowsky
Office Hours W 5:10-7:00PM CIWW 317

Course Description:

Topics vary each semester.
CSCI-GA.3033-004
(2329) Special Topics: Social Multiplayer Games Yoav Zibin
Office Hours F 1:25-3:15PM CIWW 102

Course Description:

Topics vary each semester.
CSCI-GA.3033-005
(2330) Special Topics: Big Data Application Development Suzanne McIntosh
Office Hours R 7:10-9:00PM CIWW 101

Course Description:

Topics vary each semester.

Prerequisites: Successful completion of the Realtime and Big Data Analytics course is a prerequisite for registering for this course. In addition, students should have: strong programming skills in Java, Python, or C++; experience using Hadoop; coursework in operating systems, networking, and algorithms; familiarity with Linux commands.
CSCI-GA.3033-006
(3145) Special Topics: Search Engine Architecture CANCELLED -

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-007
(2908) Special Topics: Advanced Machine Learning Mehryar Mohri
Office Hours T 5:10-7:00PM CIWW 101

Course Description:

Topics vary each semester.
CSCI-GA.3033-008
(2846) Special Topics: .Net Web Application Development Cory Plock
Office Hours M 5:10-7:00PM CIWW 101

Course Description:

Topics vary each semester.

Prerequisites: Programming languages, object-oriented programming, relational databases, and data structures.
CSCI-GA.3033-010
(3146) Special Topics: Cloud Computing Jean-Claude Franchitti
Office Hours R 7:10-9:00PM CIWW 102

Course Description:

Topics vary each semester.

Prerequisites: Some programming experience in mainstream languages such as Java, Python, C#, or C++.
CSCI-GA.3033-013
(3147) Special Topics: DevOps John Rofrano
Office Hours
Harigovind Ramasamy W 7:10-9:00PM CIWW 102

Course Description:

Topics vary each semester.

Prerequisites: Programming in either Python, Ruby, Java, or other modern OO programming language. The course will be taught using Python.
CSCI-GA.3033-014
(19894) Special Topics: Data Science for Health Lakshminarayanan Subramanian
Office Hours R 1:25-3:15PM CIWW 1302

Course Description:

Topics vary each semester.

Prerequisites: CSCI-GA 1170 or an undergraduate algorithms class, basic knowledge of statistics and at least one programming language such as Python.
CSCI-GA.3033-016
(3148) Special Topics: Multicore Processors: Architecture and Programming Mohamed Zahran
Office Hours W 5:10-7:00PM CIWW 312

Course Description:

Topics vary each semester.
CSCI-GA.3033-017
(3152) Special Topics: Abstract Interpretation Patrick Cousot
Office Hours M 5:10-7:00PM 60 Fifth Ave C12

Course Description:

Topics vary each semester.
CSCI-GA.3033-018
(3153) Special Topics: Geometric Modeling Daniele Panozzo
Office Hours R 5:10-7:00PM 60 Fifth Ave C10

Course Description:

Topics vary each semester.

Prerequisites: Basic knowledge of Computer Graphics, experience with C++ programming. Some background in geometric computing is helpful, but not necessary. CSCI-UA 140 (or an equivalent undergraduate course in linear algebra)
CSCI-GA.3033-019
(3154) Special Topics: Practical Computer Security Thitima Srivatanakul
Office Hours T 7:10-9:00PM CIWW 201

Course Description:

Topics vary each semester.

Prerequisites: Programming experience in C/C++ and Java; basic knowledge of command line UNIX tools; working knowledge of web application development.
CSCI-GA.3033-020
DS-GA.3001-005
(3149) Special Topics: Mathematics of Deep Learning Joan Bruna
Office Hours T 7:10-9:00PM CIWW 102

Course Description:

Topics vary each semester.

Prerequisites: Some background on high-dimensional probability, statistics, and/or harmonic analysis. Familiarity with general Machine Learning topics.
CSCI-GA.3033-021
(19896) Special Topics: Object-Oriented Programming Thomas Wies
Office Hours W 7:10-9:00PM CIWW 201

Course Description:

Topics vary each semester.
CSCI-GA.3033-022
(24148) Special Topics: Deep Generative Models Rajesh Ranganath
Office Hours R 5:10-7:00PM CIWW 312

Course Description:

Topics vary each semester.

Prerequisites: CSCI-GA 2567/DS-GA 1003 Machine Learning; Knowledge of probability theory, statistics, and basic neural networks.
CSCI-GA.3033-023
(24181) Special Topics: High Performance Computing for Machine Learning Alessandro Morari
Office Hours M 7:10-9:00PM CIWW 102

Course Description:

Topics vary each semester.
CSCI-GA.3812-001
(2331) Information Technology Projects * Evan Korth
Office Hours W 6:00-9:00PM CIWW 512

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
(3161) 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
(2332) 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
(2333) 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
(2334) 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
(2335) 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
(2336) 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
(2337) 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
(2338) 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
(2339) 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.3850-004
(2341) PhD Seminar: Formal Methods** Thomas Wies
Office Hours R 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.3850-006
(2746) PhD Seminar: Machine Learning** Mehryar Mohri
Office Hours T 3:45-5:00PM CIWW 1302

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
(2342) 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
(3150) Internship In Computer Science MS* Faculty -

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
(3151) Internship in Computer Science PhD* Faculty -

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

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