ITCS 3156 - Introduction to Machine Learning

Introduction to the machine learning pipeline of data collection, feature creation, algorithms, and evaluation for classification and regression based on the fundamental foundations on Linear Algebra, Probability Theory, and Optimization. The course covers basic concepts, such as training, validation, overfitting, and error rates in addition to commonly used machine learning algorithms, such as linear regression, perceptrons, naive Bayes, logistic regression, neural networks, dimensionality reduction, clustering, and reinforcement learning.

Credit Hours: (3)
Prerequisite(s): ITSC 2214 with grade of C or above and MATH 2112 with grade of C or above, or MATH 2164 and STAT 2122

Schedule of Classes