# 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