Markov Chains & Algorithmic Applications | EPFL COM 516 Course

Comprehensive course on Markov chains and their algorithmic applications, taught by Olivier Leveque at EPFL. Covers stationary distributions, ergodicity, and real-world problem-solving.

Introduction

This course covers Markov chains and their algorithmic applications, taught by Olivier Leveque at EPFL in the spring of 2020.

Highlights

• Comprehensive coverage of Markov chains, including stationary distributions, ergodicity, and convergence properties
• Exploration of various algorithmic applications of Markov chains, such as PageRank, Metropolis-Hastings algorithm, and Gibbs sampling
• Hands-on programming exercises and examples using Python

Recommendation

This course is highly recommended for students and professionals interested in probability theory, stochastic processes, and their applications in computer science and algorithms. It provides a solid foundation in Markov chains and equips learners with the tools to apply them in real-world problems.

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