Special Topics in Computer Science for Fall 2000

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

G22.3033.01 Machine Learning & Data Mining

Pre-requisite: Fundamental Algorithms

Course requirements: Problem sets, programming projects, final exam.

Textbooks:
Tom Mitchell, "Machine Learning", WCB/McGraw-Hill, 1997.
Ian Witten and Eibe Frank, "Data Mining", Morgan Kaufmann, 2000

A program "learns" if its performance on a task improves over time. In almost all cases, the key issue to make predictions about new examples based on a growing corpus of old examples. Course topics: Induction from data corpora of decision trees, rule sets, neural networks, numerical models, and probabilistic models. Bayesian learning. Nearest neighbor methods and clustering. Explanation-based learning. Evaluation and error estimation. Data preparation. Theoretical analysis: PAC learning and VC dimension.

G22.3033.09 Computational Geometry

see the course homepage


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