Foundations to Probability

1. Introduction to Probability Spaces: Introduction to representing something in a probability triplet.

2. Introduction to Independence and Conditional Probability: An introduction to intuitively understand conditional probabilities, which are instrumental in the understanding of Bayes’ Theorem.

3. Introduction to Random Variables: (Under Construction) Understanding random variables and expectation values.

4. Introcution to Bayesian Probability: (Under Construction) Introduction to Bayesian and Frequentist Statistics.

5. Introcution to Entropy and Information: (Under Construction) Introduction to the concepts of entropy and information that began with Shannon’s Information Theory.

Concepts to Probability

1. Entropy, Cross-Entropy, and KL-Divergence: (Under Construction) Expanding the concepts of information and entropy toward future mathematical applications.

2. Bayesian networks: Definitions. Representations via directed graphs. Independencies in directed models.

3. Markov random fields: Undirected vs directed models. Independencies in undirected models. Conditional random fields.

Applicatons to Probability: Machine Learning Pt1

1. Variational Autoencoder: (Under Construction) The Variational Autoencoder is a powerful generative model with deep roots in all the previous concepts of probability we have gone over.