AM207 - Stochastic Methods for Data Analysis, Inference and Optimization

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

Course Schedule

8/31

No Class

9/09

Bayesian Models

HW #0 Due

9/16

Introduction to Sampling

HW #1 Due

9/23

MCMC Sampling

HW #2 Due

9/30

Latent Variable Models & EM

Project Checkpoint #1: Team Formation

HW #3 Due

10/21

Parallel Tempering

Project Checkpoint #2: Paper Selection

HW #6 Due

11/04

Evaluating Variational Inference

Project Checkpoint #3: Paper Overview

HW #8 Due

11/16

Application of Deep Learning: Representation Learning

11/18

Application of VAEs: Counterfactual Explanations and Recourse

Project Checkpoint #4: Pedagogical Experiments

11/23

Application of Uncertainty Quantification

11/25

Holiday

11/30

Human-in-the-loop Learning and Socio-technical Systems

12/02

Ethics and Interpretability in Machine Learning

Project Due: December 17th