Course Schedule

Current semester Spring 2014, 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.1144-002
(1862) PAC II Recitation Rohit Gupta 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.1170-002
(1153) Fundamental Algorithms Recitation Alan Siegel
Office Hours R 5:10-6:00PM 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.2110-002
(1155) Programming Languages Recitation Abhinand Sundararajan R 6:10-7:00PM 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.2112-001
MATH-GA 2043-001
(1156) Scientific Computing Yu Chen R 7:10-9:00PM CIWW 1302

Course Description:
Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.

Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
CSCI-GA.2262-001
(1159) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 7:10-9:00PM CIWW 102

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.2421-001
MATH-GA 2020-001
(1161) Numerical Methods II Jonathan Goodman R 5:10-7:00PM CIWW 512

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.2440-001
(1163) Software Engineering Jean-Claude Franchitti
Office Hours R 5:10-7:00PM CIWW 101

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.2567-002
DS-GA 1003-002
(20926) Machine Learning and Computational Statistics Lab Yonatan Halpern R 8:10-9:40PM CIWW 109

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.2568-002
DS-GA 1004-002
(20928) Big Data Lab Nandita Chugh R 7:10-8:00PM CIWW 109

Course Description:

This course covers methods and tools for automatic knowledge extraction from very large datasets. Methods include on-line learning, feature hashing, class embedding, distributed databases, map-reduce framework, and applications.

Cross-listing for DS-GA 1004.

Prerequisites: DS-GA-1001 or equivalent undergraduate course, DS-GA-1002.
CSCI-GA.2715-001
NEURL-GA 3402-005
(21425) Representation and Visual Analysis Eero Simoncelli TR 10:00-11:50AM Meyr 851

Course Description:

Advanced seminars led by the faculty to provide in-depth consideration of specific topic areas in neural science. Examples of recent topics: Neurobiology of Learning and Memory, A New View of Visual Cortex, Classic Papers in Auditory Neuroscience, Auditory and Visual Cortex, Neuroimaging, Neuronal Mechanisms of Color Vision, Neural Adaptation, Nonlinear Dynamics and Neural Modeling, Neuronal Networks.

Cross-listing for NEURL-GA 3042.

Prerequisites: TBA
CSCI-GA.3033-003
(14585) Special Topics: Algorithmic & Economic Aspects of the Internet Vahab Mirrokni
Richard Cole R 5:10-7:00PM CIWW 202

Course Description:

Description: In this course, we study algorithmic and economic problems related to the Internet search, online advertisement, social networks, and online markets. We discuss important economic aspects: the ideas behind Internet ad auctions, and the game theoretic analysis of self-interested agents interacting over the Internet. We also cover the central algorithmic ideas behind the large-scale analysis of the huge data sets supporting Internet search.

Our goal is to obtain a rigorous understanding of the properties of such environments and explore these properties to solve data mining and optimization problems emerging in these environments. The main areas include computational economics, computational advertising, (social) network analysis, commerce applications, and large-scale distributed computation.

Prerequisites: Fundamental Algorithms, Mathematical Techniques for CS Applications or permission of the instructor. Also, some knowledge of basic linear algebra and probability will be assumed
CSCI-GA.3033-008
(1868) Special Topics: Realtime & Big Data Analytics Suzanne McIntosh
Office Hours R 7:10-9:00PM CIWW 101

Course Description:

This course will introduce technologies at the foundation of the Big Data movement that have facilitated scalable management of vast quantities of data collected through realtime and near realtime sensing. We will also explore the tools enabling the acquisition of near realtime data in the social domain, the fusion of those data when in flight and at rest, and their meaningful representation in graphical visualizations. Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by developing and demonstrating a software project of their choosing. Class time will be set aside for project proposal and final demo.

Prerequisites: CSCI-GA 2250 or equivalent Operating Systems course; programming experience in C/C++ or Java for assignments and final project; CSCI-GA 2262, CSCI-GA 2620, or undergraduate course in networks. A familiarity with databases will be useful.
CSCI-GA.3110-001
(14590) Honors Programming Languages Patrick Cousot
Office Hours TR 3:30-4:45PM CIWW 312

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.3700-001
MCC-GE 2295-001
(14593) Values Embodied in Information and Communication Technologies (4 credits) Helen Nissenbaum R 2:00-4:10PM EAST 207

Course Description:

Studies social, political & ethical values embodied in computer & information systems, & new media. Students examine work in the philosophy & social study of technology to understand the rich & sometimes troubling relationship between values & technical design. Course will ask: Is technology neutral? Who should make key decisions? What is the role of scientists & engineers? The course examines specific cases, such as, the Internet, search engines, web- cookies, & data mining from philosophical, empirical, & technical perspectives.

Cross-listing for MCC-GE 2295.

Prerequisites: TBA
CSCI-GA.3850-004
(1185) PhD Seminar: Formal Methods ** Thomas Wies
Office Hours R 11:00-12:30PM 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.

*

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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