Machine Learning 2
Covered kernel and Bayesian methods, probabilistic modelling, learning theory, and other paradigms of machine learning.
Mathematical Foundation for Data Science
Covered vector spaces, linear equations, orthogonality, eigenvalues, probability, multivariate normal distributions, laws of large numbers, central limit theorem, and optimization.
Machine Learning Applications
Covered Bayesian decision theory, linear models, SVMs and kernels, decision trees, expectation maximization, unsupervised learning, maximum likelihood, and Bayesian estimation.