ml.md

layout	title	permalink
page	Machine Learning	/ml/

Basics

{% include paper-card.html title="Probability Basics for ML" subtitle="Definitions, MLE, Information theory, Statistical distances" url="/ml/prob_modelling"%}

{% include paper-card.html title="Probability Distributions" subtitle="Bernoulli, Categorical, Binomial, Multinomial, Geometric, Poison, Uniform, Gaussian, Exponential, $\chi^2$, Gamma" url="/ml/distributions"%}

{% include paper-card.html title="ML Essentials" subtitle="Basics, Regularization, Ensemble Methods, Error measures" url="/ml/ml_concepts"%}

{% include paper-card.html title="Simple ML models" subtitle="kNN, Decision Trees, Naive Bayes, SVM, Logistic Regression, Linear Regression, Hierarchical Clustering, k-means, EM, Spectral Clustering" url="/ml/simple_models"%}

Generative Models

{% include paper-card.html title="Why generative models?" subtitle="Basics, Discriminative vs Generative, Use-cases, Types" url="/ml/generative_models"%}

{% include paper-card.html title="From Expectation Maximization to Variational Inference" subtitle="Latent Variable Models, EM, VI, Amortized VI, Reparametrization Trick, Mean Field VI" url="/ml/variational_inference" star="no"%}

{% include paper-card.html title="Autoregressive models (AR)" subtitle="Basics, Simplification methods, Pro/Cons, Relevant Papers" url="/ml/autoregressive_models"%}

{% include paper-card.html title="Normalizing flows" subtitle="Basics, Pro/Cons, Relevant Papers" url="/ml/flow_models"%}

Annex

{% include paper-card.html title="Variational Inference Annex" subtitle="" url="/lectures/variational_inference_annex" %}