CS 315 - Introduction to Machine Learning

Minimum Credit(s) Awarded: 4
Maximum Credit(s) Awarded: 4

In this course students will get a foundation indifferent machine learning models and algorithms. Topics may include supervised and unsupervised learning, Bayesian decision theory, neural networks, stochastic methods and cluster analysis. Students will learn how these models may solve complex real-life problems such as data mining, autonomous navigation, speech recognition, robotic control, bioinformatics, image recognition, and many others. Cross-listed NO

Offered Spring Even Years

Course Fee: No
Prerequisite(s): CS 165 or CS 179; and MATH 256