Professor Markopoulos has been teaching the Machine Learning (ML) course at The University of Texas at San Antonio since 2022 (listed as EE 4463, EE 5573, CS 6243).

The course covers the following topics.

• Foundations of supervised, unsupervised, and introductory reinforcement learning

• Mathematical principles of machine learning including linear algebra, probability, and optimization

• Core models: linear and nonlinear regression, logistic regression, support vector machines, and neural networks

• Practical aspects of model training, regularization, generalization, and evaluation

• Formal and conceptual understanding of bias–variance tradeoff, overfitting, and robustness

• Implementation of algorithms and machine learning experiments in Python using NumPy and related libraries

Before taking the course, students should have a solid understanding of the following.

• Linear Algebra: vectors, matrices, orthogonality, rank, projections, and singular value decomposition (SVD)

• Calculus: gradients, derivatives, and optimization basics

• Probability and Random Variables: independence, expectation, variance, covariance, and common distributions

• Basic Programming Skills (Python preferred)

The course has consistently received excellent evaluations. 

In Spring 2025, the course evaluations were as follows:

• Objectives and expectations clearly defined – 4.76

• Communication effectiveness – 4.68

• Preparedness for each class – 4.79

• Encouraged active learning – 4.62

• Availability outside class – 4.71

• Overall rating of the course – 4.65

• Overall rating of teaching – 4.62