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CSCI 3345 Machine Learning (Spring: 3 )


Course Description

This course provides an introduction to the field of machine learning. Specific learning paradigms to be covered include decision trees, neural networks, genetic algorithms, probabilistic models, and instance-based learning. General concepts include supervised and unsupervised adaptation, inductive bias, generalization, and fundamental tradeoffs. Applications to areas such as human-machine interaction, machine vision, bioinformatics, and computational science will be discussed.


Instructor(s): Sergio Alvarez

Prerequisites: CSCI1101 and either CSCI2245 or MATH2202 or permission of the instructor.

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Last Updated: 10-Feb-16