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Course Criteria
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3.00 Credits
Provides 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. - K. McKeown, S. Stolfo Prerequisites: COMS W3137. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
Computational approaches to natural language generation and understanding. Recommended preparation: some previous or concurrent exposure to AI or Machine Learning. Topics include information extraction, summarization, machine translation, dialogue systems, and emotional speech. Particular attention is given to robust techniques that can handle understanding and generation for the large amounts of text on the Web or in other large corpora. Programming exercises in several of these areas. - J. Hirschberg Prerequisites: COMS W3133, or W3134, or W3137, or W3139, or the instructor's permission. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
Computational approaches to speech generation and understanding. Topics include speech recognition and understanding, speech analysis for computational linguistics research, and speech synthesis. Speech applications including dialogue systems, data mining, summarization, and translation. Exercises involve data analysis and building a small text-to-speech system. - J. Hirschberg Prerequisites: Prerequisites: COMS W3133, or W3134, or W3137, or the instructor's permission. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Not offered in 2009-2010. Lect: 3. 3 pts.
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3.00 Credits
General aspects of knowledge representation (KR). The two fundamental paradigms (semantic networks and frames) and illustrative systems. Topics include hybrid systems, time, action/plans, defaults, abduction, and case-based reasoning. Throughout the course particular attention will be paid to design tradeoffs between language expressiveness and reasoning complexity, and issues relating to the use of KR systems in larger applications. Prerequisites: COMS W4701. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2-D and 3-D object representation, object recognition, vision systems and applications. - S. Nayar Prerequisites: The fundamentals of calculus, linear algebra, and C programming. Students without any of these prerequisites are advised to contact the instructor prior to taking the course. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
Introduction to robotics from a computer science perspective. Topics include coordinate frames and kinematics, computer architectures for robotics, integration and use of sensors, world modeling systems, design and use of robotic programming languages, and applications of artificial intelligence for planning, assembly, and manipulation. - P. Allen Prerequisites: COMS W3137. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3pts.
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3.00 Credits
Visual input as data and for control of computer systems. Survey and analysis of architecture, algorithms, and underlying assumptions of commercial and research systems that recognize and interpret human gestures, analyze imagery such as fingerprint or iris patterns, generate natural language descriptions of medical or map imagery. Explores foundations in human psychophysics, cognitive science, and artificial intelligence. Prerequisites: COMS W3137. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
In this course we will explore the latest advances in biometrics as well as the machine learning techniques behind them. Students will learn how these technologies work and how they are sometimes defeated. Grading will be based on homework assignments and a final project. There will be no midterm or final exam. This course shares lectures with COMS E6737. Students taking COMS E6737 are required to complete additional homework problems and undertake a more rigorous final project. Students will only be allowed to earn credit for COMS W4737 or COMS E6737 and not both. - P. Belhumeur Prerequisites: A background at the sophomore level in computer science, engineering, or like discipline. Corequisites: None General Education Requirement: Quantitative and Deductive Reasoning (QUA). 3 points
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3.00 Credits
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. - T. Jebara 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. General Education Requirement: Quantitative and Deductive Reasoning (QUA). Lect: 3. 3 pts.
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3.00 Credits
An exploration of advanced machine learning tools for perception and behavior learning. How can machines perceive, learn from, and classify human activity computationally Topics include Appearance-Based Models, Principal and Independent Components Analysis, Dimensionality Reduction, Kernel Methods, Manifold Learning, Latent Models, Regression, Classification, Bayesian Methods, Maximum Entropy Methods, Real-Time Tracking, Extended Kalman Filters, Time Series Prediction, Hidden Markov Models, Factorial HMMS, Input-Output HMMs, Markov Random Fields, Variational Methods, Dynamic Bayesian Networks, and Gaussian/Dirichlet Processes. Links to cognitive science. - T. Jebara Prerequisites: COMS W4771 or permission of instructor; knowledge of linear algebra & introductory probability or statistics is required. General Education Requirement: Quantitative and Deductive Reasoning (QUA). 3 points
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