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May 05, 2024
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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.
Offered Fall
Prerequisite(s): CS 165 or CS 179; and MATH 256
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