Lecture notes

MS&E 226: “Small” Data

These are lecture notes from the Autumn 2016 edition of the course.

Lecture 1: Introduction
Lecture 2: Linear regression
Lecture 3: More on linear regression
Lecture 4: Introduction to prediction
Lecture 5: In-sample estimation of prediction error
Lecture 6: Bias and variance
Lecture 7: Model selection
Lecture 8: Classification
Lecture 9: Logistic regression
Lecture 10: Introduction to inference
Lecture 11: Maximum likelihood
Lecture 12: Frequentist properties of estimators
Lecture 13: The bootstrap
Lecture 14: Introduction to hypothesis testing
Lecture 15: Examples of hypothesis tests
Lecture 16: Bayesian inference
Lecture 17: Additional thoughts on inference
Lecture 18: Introduction to causal inference
Lecture 19: Additional topics in causal inference

MS&E 246: Game Theory with Engineering Applications

These are lecture notes from the Winter 2007 edition of the course.

Lecture 1: Introduction
Lecture 2: The basics
Lecture 3: Pure strategy Nash equilibrium
Lecture 4: Mixed strategies
Lecture 5: Efficiency and fairness
Lecture 6: Dynamic games of complete and perfect information
Lecture 7: Stackelberg games
Lecture 8: Dynamic games of complete and imperfect information
Lecture 9: Sequential bargaining
Lecture 10: Repeated games
Lecture 11: Concluding remarks on subgame perfection
Lecture 12: Static games of incomplete information
Lecture 13: Auctions: Incomplete information
Lecture 14: Auctions: Examples
Lecture 15: Perfect Bayesian equilibrium
Lecture 16: Signaling games
Lecture 17: Network routing I
Lecture 18: Network routing II

MS&E 336: Dynamics and Learning in Games

These are lecture notes from the Spring 2007 edition of the course.

Lecture 1: Dynamic games
Lecture 2: A sequential entry game
Lecture 3: Reputation and payoff bounds
Lecture 4: Stochastic games
Lecture 6: Fictitious play
Lecture 7: Fictitious play–examples and convergence
Lecture 8: Supermodular games
Lecture 9: Adaptive learning
Lecture 10: Learning in games
Lecture 11: The multiplicative weights algorithm
Lecture 13: Blackwell’s approachability theorem
Lecture 14: Approachability and regret minimization
Lecture 15: Calibration