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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. With permission of the Instructor. CSCI1101 and either CSCI2245 or MATH2202 or permission of the instructor.

Cross listed with:


Last Updated: 24-Jun-17