Graduate Special Topics in Computer Science

NOTE: for descriptions of standard graduate computer science courses, see Graduate Course Descriptions.

G22.3033-001 Distributed Systems

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G22.3033-002 Bioinformatics

Signaling is a well-studied phenomenon both in evolutionary game theory and in cell biology. In game theory, signaling frameworks have been used to study the evolution of such fundamental phenomena as conventions and cooperation, while in biology, signal transduction has been extensively studied as a basic ingredient to multicellularity, enabling cells to communicate and coordinate. However, approaches that span both fields are scarce.

In this course, we explore the idea of viewing multicellular organisms as signaling systems in the game-theoretic sense, attempting to unify these two perspectives on signaling. A multicellular organism corresponds to a population of cells in a cooperative state, with a working signaling system in place. We will discuss how the evolution of such a system may be modeled. Then, we will in particular be interested in the breakdown of cooperation, leading to an interpretation of cancer as a disease of multicellularity.

The course will be as self-contained as possible and include introductions to evolutionary game theory and signaling systems, signal transduction in cell biology, and the biology of cancer.

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G22.3033-003 Speech Recognition

This course gives a computer science presentation of automatic speech recognition, the problem of transcribing accurately spoken utterances. The description includes the essential algorithms for creating large-scale speech recognition systems. The algorithms and techniques presented are now used in most research and industrial systems.

Many of the learning and search algorithms and techniques currently used in natural language processing, computational biology, and other areas of application of machine learning were originally designed for tackling speech recognition problems. Speech recognition continues to feed computer science with challenging problems, in particular because of the size of the learning and search problems it generates.

The objective of the course is thus not just to familiarize students with particular algorithms used in speech recognition, but rather use that as a basis to explore general text and speech and machine learning algorithms relevant to a variety of other areas in computer science. The course will make use of several software libraries and will study recent research and publications in this area.

This class is open to undergraduate students, as well as graduate students.

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G22.3033-004 Open Source Tools

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G22.3033-005 Production Quality Software

In this course, students learn to develop production quality software. Lectures present real-world development practices that maximize software correctness and minimize development time. A special emphasis is placed on increasing proficiency in a particular programming language by doing weekly development projects and participating in code reviews. Assignments become more sophisticated as the semester progresses, eventually incorporating unit tests, build scripts, design patterns, and other techniques. The course culminates with an assignment that requires students to contribute to an open-source project of their choice.

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G22.3033-006 Financial Software Projects

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G22.3033-007 Formal Methods

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G22.3033-008 Motion Capture for Gaming and Urban Sensing

This class is a research oriented project & seminar class. We cover new motion capture and vision techniques and new applications to gaming and urban sensing domains. We have a newly installed state-of-the-art motion capture system at Courant's VLG lab, as well as several research prototypes that use iPhone, web-based, and other alternative vision and motion capture based sensing, analysis, and visualization techniques. Please check http://movement.nyu.edu/mocap10f for the latest agenda for this class.

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G22.3033-009 Computer Games

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G22.3033-010 Special Topics in Statistical Natural Language Processing

In this course we will explore statistical, model based approaches to natural language processing. There will be a focus on corpus-driven methods that make use of supervised and unsupervised machine learning methods and algorithms. We will examine some of the core tasks in natural language processing, starting with simple word-based models for text classification and building up to rich, structured models for syntactic parsing and machine translation. In each case we will discuss recent research progress in the area and how to design efficient systems for practical user applications.

In the course assignments you will construct basic systems and then improve them through a cycle of error analysis and model redesign. This course assumes a good background in basic probability and a strong ability and interest to program in Java. The class is open to graduate as well as undergraduate students.

See the course homepage for more information.


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