AI 231 - Machine Learning Fundamentals

3 Credit Hours

This course introduces the fundamental principles, models, and algorithms of machine learning. Students explore supervised and unsupervised learning paradigms, including regression, classification, and clustering. Emphasis is placed on model evaluation, overfitting prevention, and hyperparameter optimization. Students gain practical experience through Python-based implementation using Scikit-learn and TensorFlow.
Prerequisite: A “C” or better in:

CS 231 - Computer Programming II

AND MAT 335 - Linear Algebra

AND MAT 355 - Discrete Mathematics

Lecture Hours: 3
Term(s) Offered: Spring