CSCI 374
Machine Learning
Spring 2013
Division III
Quantitative/Formal Reasoning
This is not the current course catalog
Class Details
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.
The Class:
Format: tutorial
Limit: 10
Expected: 10
Class#: 3116
Grading: OPG
Limit: 10
Expected: 10
Class#: 3116
Grading: OPG
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
Prerequisites:
Computer Science 136 and Mathematics 251; Computer Science 256 is recommended but not required
Enrollment Preferences:
Computer Science majors
Distributions:
Division III
Quantitative/Formal Reasoning
Attributes:
COGS Interdepartmental Electives
Class Grid
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HEADERS
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CLASSESColumn header 2DREQColumn header 3INSTRUCTORSColumn header 4TIMESColumn header 5CLASS#
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CSCI 374 - T1 (S) TUT Machine Learning
CSCI 374 - T1 (S) TUT Machine LearningDivision III Quantitative/Formal ReasoningTBA3116
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