Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Monte Carlo: Markov ChainsCovers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Stochastic Simulation: FundamentalsIntroduces stochastic simulation fundamentals, covering course organization, queueing models, finance, statistics, physics, and exam details.
Detection & EstimationCovers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Jacamar Data AnalysisCovers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
