CS 5520 Fundamental Machine Learning 3.0 Credits
This course is a breadth survey of traditional supervised and unsupervised machine learning algorithms that lay the groundwork for state-of-the-art techniques. It also introduces core machine learning concepts such as data set, evaluation, overfitting, regularization and more. The course focuses on the underlying mathematical principles of these algorithms (probability and statistics, gradient-based learning, principal component analysis, etc.) and students implement various algorithms from scratch, without the use of any frameworks or APIs, in addition to using existing modern frameworks.
Repeat Status: Not repeatable for credit
Prerequisites: (CS 5010 [Min Grade: D] or CS 501 [Min Grade: D]) and (CS 5030 [Min Grade: D] or CS 502 [Min Grade: D])