CMPE 427 - Introduction to Machine Learning for Engineers

Units: 3
Broad introduction to classical machine learning and deep learning. Principles of supervised, unsupervised, semi-supervised, reinforcement and fuzzy learning. Deep learning methods and applications. Students will gain understanding of mathematical models of major approaches and hands-on experience with industry-standard software.

Prerequisites: MATH 130; ENGR 215 or CS 301.
Possible Instructional Methods: Entirely On-ground.
Grading: A-F grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Understand machine learning concepts and mathematical models;
Apply state-of-the-art software and methods for classification;
Analyze real-world data sets to perform prediction;
Evaluate learning methods to produce solutions to real-world problems.