Date 
Topics 
Lecture Slides/Notes 
Reading 




9/8 
Introduction 
Lecture 1: slides

(Mitchell) Chapter 1
(Murphy) Chapter 1 (optional)

9/12 
Decision Trees 
Lecture 2: slides

(Mitchell) Chapter 3

9/15 
Random Forests

Lecture 3: slides

Random Forests chapter of ESL (optional)  reading guide

9/19 
Evaluation and Model Selection

Lecture 4: slides

(Mitchell) Chapter 5

9/22 
Boosting

Lecture 5: slides

Boosting book  Chapter 1 (optional)

9/26 
Neural Networks I: Intro, Linear separators 
Lectures 6–8: slides

(Mitchell) Chapter 4
(Murphy) Chapter 13 (optional)  reading guide

9/29 
Neural Networks II: Perceptron, Gradient descent 


10/3 
Neural Networks III: Gradient descent, SGD, Sigmoid units, Multilayer networks, Backprop, Reducing overfitting 


10/6 
SVMs: Large margin separation, Softmargin SVM, learning with kernels

Lectures 9–10: slides

SVM tutorial  reading guide
Andrew Ng's SVM lecture notes (optional)

10/10 
Midterm 


10/13 
Softmargin SVM, Learning with kernels 


10/17 
Probability Review, Maximum Likelihood Estimation 

Estimating Probabilities: MLE and MAP
(Murphy) Chapter 4 (optional)  reading guide

10/20 
MAP Estimation (including MDL) 
Lecture 12: slides

Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression
(Murphy) Chapters 9 and 10 (optional)  reading guide
(Mitchell) Section 6.6: MDL Principle

10/24 
Naive Bayes 
Lecture 13: slides


10/27 
Naive Bayes continued, Logistic Regression
Learning Theory

Lectures 14–15: slides

(Mitchell) Chapter 7 (up to and including Section 7.4.3)

10/31 
Learning Theory: PAC Learning, Agnostic Learning, VC dimension 


11/3 
Clustering I: Kmeans problem and Hierarchical clustering 
Lecture 16: slides


11/7 
Clustering II: Kmeans problem and Hierarchical clustering
Instancebased Learning I: kNN and recommender systems 
Lecture 17: slides

(Mitchell) Chapter 8 (Sections 8.1 and 8.2)

11/10 
Instancebased Learning II: kNN and recommender systems




Reading Break 


11/17 
Gaussian mixture models and EM

Lecture 19: slides/notes (from Spring 2022)
Jupyter notebook for EM

(Murphy, 2012) Chapter 11  reading guide

11/21 
Dimension Reduction/Feature Transformation: PCA I

Lecture 20:
slides
Jupyter notebook for Eigenfaces

Jonathon Shlens's PCA tutorial (Sections I through V)

11/24 
Dimension Reduction/Feature Transformation: PCA II
Fairness and Machine Learning 
Lecture 21:
slides


11/28 
Project Presentations (in class) 


12/1 
Project Presentations (in class) 

