Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
