Lectures related to Machine Learning Fundamentals

Covers PCA application and BBP phase transition in a card game dataset.

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Covers the concept of pKa values in solution chemistry and how to obtain accurate constants.

Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.