The material on this page is provided to aid the understanding of artificial intelligence, machine learning, and reinforcement learning for practitioners. I will periodically update and expand the material on this page. While having a sound grasp of the basics of linear algebra, calculus, optimization, probability theory, and statistics is essential for practitioners, it is insufficient for someone interested in understanding and producing technical research in the field. While there is an abundance of mathematics for machine learning books and online lecture notes, there appears to be a significant gap for someone seeking to acquire the level of mathematical sophistication to do research in the field. A new section will be added to this page that provides the mathematical coverage at an advanced level to fill the gap.
Introductory Mathematics for AI/ML/RL: These notes capture the core mathematical requirements to support understanding AI/ML/RL technical details at the introductory-intermediate level. The material in this section will allow you to comprehend some of the technical aspects of the plethora of machine learning algorithms and approaches.
Notes
Probability Theory and Statistics
Introduction to Machine Learning: An introductory background in statistical learning theory (SLT) is essential for the development of sound algorithms and for data scientists. Below, I provided some of the significant topics in SLT that will support a deeper understanding of learning algorithms and their potential pitfalls. While deep and generative learning is at the center of the buzz in AI, they are not a solution to all analytical tasks. Data scientists should also be grounded in the classical methods, as often they are the right tool for the job.
Notes
Reinforcement Learning (TBD):
Advanced Mathematics for AI/ML/RL (TBD):
Real Analysis
Measure Theory
Probability Theory
Functional Analysis
© 2025-2030 RWAnalytics. All rights reserved.