Stochastic Methods for Data Analysis, Inference & Optimization | Harvard University

Explore the powerful techniques of Monte Carlo methods and their applications in data analysis, inference, and optimization at Harvard University.

Introduction

Monte Carlo methods are a diverse class of algorithms that rely on repeated random sampling to compute the solution to problems whose solution space is too large to explore systematically or whose systemic behavior is too complex to model. This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications.

Highlights

• Covers basic ideas of Bayesian analysis and Markov chain Monte Carlo samplers
• Explores more recent developments such as slice sampling, multi-grid Monte Carlo, Hamiltonian Monte Carlo, parallel tempering and multi-nested methods
• Investigates streaming methods like particle filters/sequential Monte Carlo
• Delves into related topics in stochastic optimization and inference such as genetic algorithms, simulated annealing, probabilistic Gaussian models, and Gaussian processes
• Applications to Bayesian inference and machine learning are used throughout

Recommendation

This course is recommended for students interested in learning about the powerful techniques of Monte Carlo methods and their applications in data analysis, inference, and optimization. It provides a comprehensive understanding of the fundamental principles and recent advancements in this field, making it a valuable resource for those pursuing careers in data science, machine learning, and computational science.