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MTE 888

Intelligent Systems

Machine learning and pattern classification are fundamental blocks in the design of an intelligent system. This course will introduce fundamentals of machine learning and pattern classification concepts, theories, and algorithms. Topics covered include: Bayesian decision theory, linear discriminant functions, multi-layer neural networks, classifier evaluation, and an introduction to unsupervised clustering/grouping, and other state-of-the-art machine learning and AI algorithms.
Weekly Contact: Lab: 1 hr. Lecture: 3 hrs.
GPA Weight: 1.00
Course Count: 1.00
Billing Units: 1

Prerequisites

(ELE 632 or MTE 501) and (MEC 733 or MTE 502)

Co-Requisites

None

Antirequisites

None

Custom Requisites

None

Mentioned in the Following Calendar Pages

*List may not include courses that are on a common table shared between programs.