Machine Learning

Probability Basics for ML

Definitions, MLE, Information theory, Statistical distances

Probability Distributions

Bernoulli, Categorical, Binomial, Multinomial, Geometric, Poison, Uniform, Gaussian, Exponential, $\chi^2$, Gamma

ML Essentials

Basics, Regularization, Ensemble Methods, Error measures

Simple ML models

kNN, Decision Trees, Naive Bayes, SVM, Logistic Regression, Linear Regression, Hierarchical Clustering, k-means, EM, Spectral Clustering

Why generative models?

Basics, Discriminative vs Generative, Use-cases, Types

From Expectation Maximization to Variational Inference

Latent Variable Models, EM, VI, Amortized VI, Reparametrization Trick, Mean Field VI

Autoregressive models (AR)

Basics, Simplification methods, Pro/Cons, Relevant Papers

Normalizing flows

Basics, Pro/Cons, Relevant Papers

Variational Inference Annex