CSCI 374 Spring 2013 Machine Learning (Q)

This tutorial examines the design, implementation, and analysis of machine learning algorithms. Machine Learning is a branch of Artificial Intelligence that aims to develop algorithms that will improve a system's performance. Improvement might involve acquiring new factual knowledge from data, learning to perform a new task, or learning to perform an old task more efficiently or effectively. This tutorial will cover examples of supervised learning algorithms (including decision tree learning, support vector machines, and neural networks), unsupervised learning algorithms (including k-means and expectation maximization), and possibly reinforcement learning algorithms (such as Q learning and temporal difference learning). It will also introduce methods for the evaluation of learning algorithms, as well as topics in computational learning theory.
Class Format: tutorial
Requirements/Evaluation: evaluation will be based on presentations, problem sets, programming exercises, empirical analyses of algorithms, critical analysis of current literature, and a final exam
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Prerequisites: Computer Science 136 and Mathematics 251; Computer Science 256 is recommended but not required
Enrollment Preference: Computer Science majors
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Divisional Attributes: Division III,Quantitative and Formal Reasoning
Other Attributes: COGS Interdepartmental Electives
Enrollment Limit: 10
Expected Enrollment: 10
Class Number: 3116
CLASSES ATTR INSTRUCTORS TIMES CLASS NUMBER
CSCI374-T1(S) TUT Machine Learning (Q) Division 3: Science and MathematicsQuantitative and Formal Reasoning Andrea Danyluk
TBA 3116
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