AM207 - Stochastic Methods for Data Analysis, Inference and Optimization


This is a introductory graduate course on probabilistic modeling and inference for machine learning.

General Course Information

Course Instructor

What is this Course?

The aim of this course is to help students develops skills for computational research with focus on stochastic approaches, emphasizing implementation and examples. Stochastic methods make it feasible to tackle very diverse problems when the solution space is too large to explore systematically, or when microscopic rules are known, but not the macroscopic behavior of a complex system. Methods are illustrated with examples from a wide variety of fields, like demography, health-care, and finance. We tackle Bayesian methods of data analysis as well as various stochastic optimization methods. Topics include stochastic optimization (stochastic gradient descent and simulated annealing), Bayesian models and approximate inference methods (Markov chain Monte Carlo and variational inference).

Important Course Documents

Course Materials

The following links provide access to: 1) lecture videos 2) lecture notes 3) lecture note summaries 4) in-class exercises and 5) homework.

Lecture videos aims to cover high level ideas to help you build intuition and make connections between different concepts. There are many details that are necessary for gaining a deeper understanding of a subject, and students are expected to study these in addition to watching the lecture videos. The notes for each lecture is contained in a Jupyter Notebook (e.g. lecture_1_notes.ipynb), and typically cover topics addressed in lecture videos in greater levels of detail. For each lecture, there is also a summary providing high level synthesis of topics as well as explorations of their broader social impact.