Fall 2008

Course webpage: http://www.cs.columbia.edu/~kar/4444f08/ GRADE: A- Description: One of the most exciting courses at Columbia University. The class consisted of 18 students(Undergrads, Master's & PhDs) We competed in teams of 3, to solve different problems such as bin packing, game playing and graph drawing using innovative techniques such as physics engines. The most fun was near the end when we resorted to on the fly - quick and dirty - optimizations of our algorithms that would give us an edge over the opponent. This course is conducted every year in Fall by Prof Ken Ross PS: Do not miss out on the T-Shirt activity!

Taught by: Prof. Kathy McKeown Course webpage: http://www1.cs.columbia.edu/~kathy/NLP/ GRADE: B Description(from website): This course provides an introduction to the field of computational linguistics, aka natural language processing (NLP). We will learn how to create systems that can understand and produce language, for applications such as information extraction, machine translation, automatic summarization, question-answering, and interactive dialogue systems. The course will cover linguistic (knowledge-based) and statistical approaches to language processing in the three major subfields of NLP: syntax (language structures), semantics (language meaning), and pragmatics/discourse (the interpretation of language in context). Homework assignments will reflect research problems computational linguists currently work on, including analyzing and extracting information from large online corpora.

Taught by: Prof Rocco Servedio. Course webpage: http://www.cs.columbia.edu/~rocco/Teaching/F08/cs4252/ GRADE: B- Description(from website):The question "Can machines learn from experience?" is one that has fascinated people for a long time. Over the past few decades, many researchers in computer science have studied this question from a range of applied and theoretical perspectives. This course will give an introduction to some of the central topics in computational learning theory. We will study well-defined mathematical models of learning in which it is possible to give precise and rigorous analyses of learning problems and learning algorithms. A big focus of the course will be the computational efficiency of learning in these models. We'll develop computationally efficient algorithms for certain learning problems, and will see why efficient algorithms are not likely to exist for other problems.

Taught by: Sergei Vassilvitskii & Sébastien Lahaie. Course webpage: http://www.cs.columbia.edu/coms6998-3/ GRADE: Took Pass Fail!...and passed! Description(from website):Algorithmic game theory is an emerging area at the intersection of computer science and microeconomics. Motivated by the rise of the internet and electronic commerce, computer scientists have turned to models where problem inputs are held by distributed, selfish agents (as opposed to the classical model where the inputs are chosen adversarially). This new perspective leads to a host of fascinating questions on the interplay between computation and incentives. This course provides a broad survey of topics in algorithmic game theory, such as: algorithmic mechanism design; combinatorial and competitive auctions; congestion and potential games; computation of equilibria; network games and selfish routing; and sponsored search. No prior knowledge of game theory is necessary; the most important prerequisite is mathematical maturity.

Spring 2009

Taught by: Prof Jason Nieh. Course webpage: http://www.cs.columbia.edu/~nieh/teaching/e6998/ GRADE: NA Description(from webpage): Study of mobile computing on smartphones with an emphasis on applications. These handheld Internet devices are poised to become the future dominant software platform as a result of the rapid convergence of computers and mobile phones. Topics covered will include mobile operating systems and development environments, input modalities and user interfaces for mobile devices, power management issues for mobile devices, wireless mobile networking, thin clients and mobile Web, location-aware and other context-aware services, and virtualization. A course programming project will be required.

Taught by: Prof Gitanjali Swamy. Course webpage: http://www1.cs.columbia.edu/~gms2155/ GRADE: NA Description(from webpage): This course will use a combination of analytic and case based pedagogy. Many of the sections will be covered by guest lecturers who have built the systems under discussion. The course will introduce a wide variety of financial tools ranging from Bloomberg, CapitalIQ, VentureXpert, Moody’s and Aarm. The final exercise will consist of a paper that either uses the suite of tools to solve a financial problem in a new way or design of a technology that could change financial business models.

Taught by: Prof Dragomir Radev. Course webpage: http://belobog.si.umich.edu/clair/ir09/ GRADE: NA Description(from webpage): A significant portion of the information that surrounds us is in textual format. A number of techniques for accessing such information exist, ranging from databases to natural language processing. Some of the most prestigious companies these days spend large amounts of money to build intelligent search engines that allow casual users to find what they want anytime, from anywhere, and in any language. In this course, we will cover the theory and practice behind the implementation of search engines, focusing on a wide range of topics including methods for text storage and retrieval, the structure of the Web as a graph, evaluation of systems, and user interfaces.

Taught by: Prof Gail Kaiser. Course webpage: http://bank.cs.columbia.edu/classes/cs6125/ GRADE: NA Description(from webpage):History of hypertext, markup languages, groupware and the Web. Evolving Web protocols, formats and computation paradigms such as HTTP, XML and Web Services. Novel application domains enabled by the Web. This course does not address lower-level Internet protocols such as SIP and RTP. Students interested in transport-level Internet services should instead take E6181 Advanced Internet Services. Workload: Individual research paper, individual or group project, individual presentation. Students should plan to attend class on a regular basis.