Week 2

Lecture

• Lecture #2: Maximum Likelihood Estimation
  • Lecture Video
  • Lecture Notes
  • Lecture Summary
• Lecture #3: Bayesian Modeling
  • Lecture Video
  • Lecture Notes
  • Lecture Summary

Activities

• In-Class Exercise #2: Maximum Likelihood Estimation
• In-Class Exercise #2: Solutions
• In-Class Exercise #3: The Beta-Binomial Model
• In-Class Exercise #3: Solutions
• Homework #1
• Homework #1 Solutions

Reading

Applications and Broader Impact

1. (Introductory) Second opinion needed: communicating uncertainty in medical machine learning
2. (Introductory) Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
3. (Introductory) Interpretability (Chapter 2 of Interpretable Machine Learning: A Guide for Making Black Box Models Explainable)
4. (In-Depth) Towards A Rigorous Science of Interpretable Machine Learning

Optimization

1. (Introductory) Simple Multivariate Optimization
2. (Introductory) More on Constrained Optimization Using Lagrange Multipliers
3. (Advanced) Convex Optimization Overview

Statistics

1. (Introductory) Philosophy and the practice of Bayesian statistics
2. (Introductory) The Exponential Family: Conjugate Priors

Other Reference

1. Gelman, Chapters 2 & 3
2. Gelman, Chapter 4