Course Schedule

Current semester Spring 2016, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1144-001
(2506) 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.2250-002
(18717) Operating Systems Allan Gottlieb
Office Hours T 5:10-7:00PM CIWW 517

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.2421-001
MATH-GA 2020-001
(2514) Numerical Methods II Jonathan Goodman 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.2590-001
(3078) Natural Language Processing Ralph Grishman
Office Hours T 5:10-7:00PM CIWW 109

Course Description:

How can we teach machines to understand human languages so that they can answer queries, summarize dense information, or hold a conversation with us? The primary goal of this course is to provide students with the principles and state-of-the-art tools needed to solve a variety of NLP problems. We will focus on three paradigms in the field of NLP: supervised learning, pretrain-then-finetune, and the most recent large language models, with a focus on representation learning techniques. Students are expected to read research papers and gain hands-on experience through coding assignments.

Prerequisites:
Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235); Algorithms and data structure (CSCI-UA.102); Basic knowledge in machine learning (DS-GA.1003, CSCI-UA.0473). We will not spend a significant amount of time on machine learning basics so some prior exposure to the supervised learning framework (e.g., loss functions, SGD) is expected. Good comfort level with Python
CSCI-GA.3033-004
(2522) Special Topics: Social Multiplayer Games Yoav Zibin
Office Hours T 5:10-7:00PM CIWW 317

Course Description:
The course will teach how to develop online casual multiplayer games using open technologies such as HTML5, JavaScript, Typescript, AngularJS, Ionic, Bootstrap, PhoneGap, Grunt, and testing using Karma, Jasmine, Protractor. Students will build several turn based games and submit them to Google play store. There is no final exam, only assignments. The course will also teach about artificial-intelligence for games, ranking systems, physics-based engines, and integrating with social platforms. The course will not concern with 3D graphics.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-007
(3456) Special Topics: Advanced Machine Learning Mehryar Mohri
Office Hours T 5:10-7:00PM CIWW 101

Course Description:
This course introduces and discusses advanced topics in machine learning. The objective is both to present some key topics not covered by basic graduate ML classes such as Foundations of Machine Learning, and to bring up advanced learning problems that can serve as an initiation to research or to the development of new techniques relevant to applications.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-015
(20443) Special Topics: Information and Communication Technology for Developing Regions Jay Chen
Office Hours T 7:10-9:00PM CIWW 101

Course Description:
Computer science research has traditionally focused on technical challenges that are relatively agnostic to the contexts in which technology is eventually used. Recently, however, global challenges such as those found in international development and climate change have begun to shift our collective attention toward applying technology for the greater social good. In this course, we will explore how Information and Communication Technologies (ICTs) can be leveraged toward addressing large-scale societal problems in agriculture, healthcare, education, finance, and sustainability. Applying ICTs to solve problems in this space is a challenging and increasingly inter-disciplinary task involving several areas within computer science including: networking, systems, databases, security, and human-computer interaction. In this course, we will review some of the past and very recent efforts in this space, examine in detail the specific contextual challenges, and explore potential solutions to these important problems. The course is accessible to students from a variety of backgrounds including computer science students, ITP students, MSIS students and students in specific application domains (healthcare, finance, education). It is important that any student taking this class have basic exposure to IT courses. In any project group, it is very important that at least a few if not all the students have some coding experience in languages such as C/C++/Java. Most of the projects do not require hacking skills but definitely require reasonable amount of coding. Application specific exposure/expertise is a huge plus in any project.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3110-001
(3268) Honors Programming Languages Patrick Cousot
Office Hours TR 3:30-4:45PM CIWW 412

Course Description:

The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.

Prerequisites: CSCI-GA.2110 Programming Languages, CSCI-GA.3110 Honors Programming Languages
CSCI-GA.3230-001
MATH-GA.2932-001
(18721) Random Graphs Joel Spencer
Office Hours T 5:10-7:00PM CIWW 102

Course Description:
This course covers numerous topics related to random graphs, including generalized randomized structures, random processes, probabilistic methods and Erdös Magic. Also covered are branching processes, phase transitions for large random evolutions, derandomization via conditional expectations and semidefinite programming derandomization techniques. Algorithms, probability and discrete mathematics all appear, but concepts will be defined from scratch. Emphasis will be on methods of asymptotic calculation.
CSCI-GA.3850-001
(2534) PhD Seminar: Cryptography** Yevgeniy Dodis
Office Hours T 12:30-3:15PM CIWW 412

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
(3081) 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.

*

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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