Date 
Topics 
Lectures and Assignments 
Reading 




1/10 
Introduction 
Lecture 1: slides

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

1/11 
Decision Trees I 
Lectures 2–3: slides

(Mitchell) Chapter 3

1/13 
Decision Trees II 


1/17 
Decision Trees and Random Forests

Lecture 4: slides

Random Forests chapter of ESL (optional)  reading guide

1/18 
Neural Networks I: Intro 
Lectures 5–9: slides

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

1/20 
Neural Networks II: Linear separators 


1/24 
Neural Networks III: Perceptron, Gradient descent, SGD 


1/25 
Neural Networks IV: Sigmoid units, Multilayer networks, Backprop 


1/27 
Neural Networks V: Dealing with overfitting 


1/31 
SVMs I: Large margin separation 
Lectures 10–11: slides

SVM tutorial  reading guide

2/1 
SVMs II: Softmargin SVM 

Andrew Ng's SVM lecture notes (optional)

2/3 
Learning with kernels Probability Review 
Lecture 12: probability slides


2/7 
Maximum Likelihood Estimation 

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

2/8 
MAP Estimation (including MDL) 
Lecture 14: slides


2/10 
Midterm 


2/14 
Naive Bayes 
Lectures 15–16: slides

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

2/15 
Logistic Regression 


2/17 
Evaluating the performance of hypotheses and Model selection 
Lecture 17: slides

(Mitchell) Chapter 5


Reading Break 


2/28 
No class because of "snow" 

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

2/29 
Learning Theory: PAC Learning 
Lecture 18: slides/notes


3/3 
Learning Theory: Agnostic Learning 
Lecture 19: slides/notes


3/5 
Learning Theory: VC Dimension (makeup lecture) 
Lecture 20: slides/notes, video (Spring 2021)


3/7 
Instancebased Learning: kNN and recommender systems 
Lecture 21: slides

(Mitchell) Chapter 8 (Sections 8.1 and 8.2)

3/8 
Instancebased Learning continued 


3/10 
Clustering I: Kmeans problem 
Lecture 23–24: slides


3/14 
Clustering II: Hierarchical clustering 


3/15 
Gaussian mixture models and EM 
Lectures 25–26: slides/notes (from Spring 2022)

(Murphy, 2012) Chapter 11  reading guide

3/17 
Gaussian mixture models and EM continued 
Jupyter notebook for EM


3/21 
Dimension Reduction/Feature Transformation: PCA I 
Lectures 27–28:
slides/notes

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

3/22 
Dimension Reduction/Feature Transformation: PCA II 
Jupyter notebook for Eigenfaces


3/24 
Dimension Reduction/Feature Transformation: ICA

Lecture 29: slides
Jupyter notebook on statistical independence


3/28 
Boosting I

Lectures 30–31: slides

Boosting survey (optional reading)

3/29 
Boosting II



3/31 
Fairness and Machine Learning 
Lecture 32: slides


4/4 
Project Presentations (in class) 


4/5 
Project Presentations (in class) 

