Hidden Markov Models (HMM): Theory

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Linear Regression: Estimation and Prediction

Covers the basics of linear regression, focusing on estimation and prediction.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Markov Chains and Applications

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Dependability Evaluation in Industrial Automation

Explores dependability evaluation, preventive maintenance, reliability, Markov models, FMEA, FTA, and software safety integrity in industrial automation.

Markov Chains and Algo Applications

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.

Geometric Ergodicity: Convergence Diagnostics

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.