Number 
Date 
Topic 
Reading 
Slides/Handouts 
Homework 
1 
9/1 Tue 
What is machine learning? Illustration
through linear models. 
HW0: Getting comfortable with python. 

2 
9/3 Thu 
Linear regression. Intro to basic elements of
ML: data, model, sampled training data, algorithm/training,
testing (aka generalization). 

3 
9/8 Tue 
Peek under the hood of the optimization
engine. Quick refresher of derivatives and gradients.
Gradient descent: the "natural" algorithm. 
Programming HW 1: Linear regression in python 

4 
9/10 Thu 
Modifying learning via changes to cost
function. Logistic and multiclass regression. l_2 and l_1
regularizers and their uses in preventing overfitting. (bias
towards sparsity). 

5 
9/15 Tue 
Quick tour of "datascience" through the
lens of linear models. kfold cross validation.
Regressionbased analysis in economics, and neuroscience.
Interpretability and its pitfalls. (Q&A with guest.) 
Programming HW 2: Data science explorations.
Effect of regularization. 

6 
9/17 Thu 
Unsupervised learning and its goals. Linear
algebra refresher (vectors, inner products, distance).
Clustering via kmeans. 

7 
9/22 Tue 
Lowdimensional representation of high
dimensional data. Connections to eigenvalues/eigenvectors.
("SVD"); describe but not prove. Eigengenes and Eigenfaces. 
Programming HW 3: Clustering and
lowdimensional representation 

8 
9/24 Thu 
Refresher of elementary probability (coin
tossing, conditional probability and bayes rule). Simple
production models for text. ngrams. 

9 
9/29 Tue 
How to evaluate a probabilistic model of
text. Perplexity. Training vs test. (Detecting overfitting.)

Programming HW 4: ngram model? matrix factorization? Genomics or neuroscience? 

10 
10/01 Thu 
Word embeddings and their fun properties.
Constructing word embeddings from ngram models. (matrix
factorization) 

11 
10/06 Tue 
Lore of Gaussians. Understanding sampling and
generalization a bit better. (optional; may skip if running
late) The nonreproducibility problem in experimental social
science. 

12 
10/8 Thu 
Midterm review  Takehome midterm (timed)  
13 
10/15 Thu 
Deep nets. Loss function and gradients. Chain rule  
14 
10/20 Tue 
Backpropagation algorithm.  Programming HW 5: Implementing a simple deep net.  
15 
10/22 Thu 
Convolutional nets and computer vision.  
16 
10/27 Tue 
Convnets contd  Programming HW 6: ConvNets for vision.  
17 
10/29 Thu 
Recurrent nets and applications.(optional;
can skip if running late) 

18 
11/03 Tue 
Decisionmaking under uncertainty: basic elements. (Rational choice framework; expected utility/cost)  No programming HW. 

19 
11/05 Thu 
Markov Decision Processes (MDP) as a model
for decisionmaking in a stochastic environment.Optimum
policy 

20 
11/10 Tue 
Computing optimum policy. (Policy
gradient) Guest minilecture by Elad Hazan on linear control
and ventilators. 
Programming HW7: Computing and using an optimum policy  
21 
11/12 Thu 
(Guest lecture) Gameplaying and Alpha Go.  
22 
11/17 Tue 
Ethics in ML. Explainability, bias,
transparency, proper data collection. (Guest lecturer:
? Maybe Arvind) 

23 
11/19 Thu 
(Guest lecture) Deep Learning and Language Models  
24 
11/24 
Ask me anything. (Finals review during
reading period.) 