Lectures related to Markov Chain Monte Carlo: Sampling High-Dimensional Distributions

Maximum Entropy Modeling: Applications & Inference

Explores maximum entropy modeling applications in neuroscience and protein sequence data.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.