Computer Science


This listing represents a sample of courses offered by several departments. See the Statistics Summer Courses and Computer Science Summer Courses pages for complete lists.

STAT S4240D. Data Mining. 3 pts.

Prerequisites: COMS W1003 (Intro to Comp Science/Prog in C), W1004 (Intro to Comp Science/Prog Java), W1005 (Intro to Comp Science/Prog MATLAB) , W1007 (Honors Intro to Comp Science), or the equivalent.

This course will provide an overview of current research in data mining and will be suitable for graduate students from many disciplines. Specific topics covered will include databases and data warehousing, exploratory data analysis and visualization, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, and causal inference.
see department listings

COMS S4701D. Artificial Intelligence. 3 pts.

Prerequisites: COMS W3134 (Data structures in Java), W3136 (Data Structures with C/C++), or W3137 (Honors Data Structures and Algorithms)

This course will provide a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included, as time permits.
see department listings

COMS S4771D. Machine Learning. 3 pts.

Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence.

Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in Matlab.
see department listings

The New York
City Experience

Meet Tech Elite
Try to get into the oft sold-out NY Tech Meetup or watch it online! The monthly event draws venture capitalists, tech entrepreneurs, developers, and creative professionals to network and demo tomorrow’s biggest apps. Past guests include Foursquare and Tumblr.

Laser Vision
With some 40 events, workshops, and performances each year, the Brooklyn-based Eyebeam is a leading art and technology nonprofit. Their innovative cultural programming – including live performances and workshops – make it worth the trip to Sunset Park.

Hack to the Future
New York’s hippest borough is home not only to artisanal cuisine but also a booming tech industry. Ride the East River Ferry to DUMBO, refuel at Brooklyn Roasting Company, and visit locales including the Made in NY Media Center, an incubator for promising tech and media startups.