Covers the estimation of distributions using various methods such as minimum loss and expectation.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
