CS 4033/5033 Machine Learning Class schedule

Fall 2019

The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2019. The schedule will be updated throughout the semester as we delve deeper into special topics and/or add additional topics of interest. The project deadlines are in red and will not change, even if other parts of the schedule change.

Date Topic Assigned Reading Assigned today Due today
Week 1: Introduction
Aug 19 Overview of the course. What is machine learning? What will I learn if I take this class? Elements of Statistical Learning (ESL), Chapter 1   Pretest (in class)

Aug 21


Project introduction, Introduction to ML & Reinforcement learning , Exploration & Exploitation Sutton & Barto: Chapters 1 & 2 Projects  
Week 2: Reinforcment Learning
Aug 26 The RL problem Sutton & Barto: Chapter 3 HW 1
Aug 28 The RL Problem      

Week 3: Reinforcment Learning

Sep 2
Labor Day (no class)
Sep 4

In-class RL project proposals


In-class RL project proposals

HW 1

Week 4: Reinforcement learning
Sep 9 RL problem     Written RL project proposals
Sep 11

RL Problem, TD learning, Q-learning

Sutton & Barto: Chapter 6 HW 2  
Week 5: Reinforcment learning
Sep 16 TD learning, Q-learning    


Sep 18

RL using function approximation

Sutton & Barto: Chapter 9

Week 6: Supervised learning

Sep 23

Eligibility Traces

Sutton & Barto: Chapter 7 and 12


HW 2

Sep 25

Introduction to SL, Nearest Neighbor, K-means clustering

Least Squares Linear Regression, Logistic regression, Introduction to regularization

ESL Chapter 2.1-2.3, ESL Chapter 3.1-3.2

Logistic: Wikipedia and ESL Chapter 4.4

HW 3  
Week 7: Model evaluation and short RL projects

Sep 30

Ridge, Lasso, and Elastic nets, Overfitting, Bias-Variance Tradeoff, Model selection

Ridge/Lasso/Elastic: ESL Chapter 3.3-3.8


Oct 2

In-class Short RL project presentations

Model evaluation and verification

ESL Chapter 7  

Short RL project presentations

Long RL checkpoint

HW 3: Friday Oct 4

Week 8: Short RL projects, model verification
Oct 7

In-class Short RL project presentations

Finish Model evaluation and verification


HW 4 (one question) Written Short RL projects
Oct 9

In-class Long SL Project Proposals

  HW 5

Long SL Project Proposals

Week 9: Trees

Oct 14




Written Long SL project proposals

Oct 16

Decision trees

Trees: ESL Chapter 9.2

  HW 4
Week 10: Tree-based methods and Ensembles

Oct 21

Trees and random forests

Boosting/Bagging/Ensembling: ESL Chapter 8.7, ESL Chapter 10, ESL Chapter 15 HW 6 HW 5

Oct 23

Ensemble methods, Boosting, Bagging, Random Forests and Gradient Boosted Regression Trees



Week 11: Long RL projects
Oct 28

In-class Long RL project presentations

    Long RL project presentations

Oct 30

In-class Long RL project presentations



Week 12: Neural Nets, Deep learning
Nov 4

Neural Networks, Backpropagation

Neural Networks and Deep Learning chapter 6 OR

Deep Learning Book


Written long RL projects

Nov 6

Online Short SL project proposals

Neural Networks, Backpropagation, CNNs

Neural Networks and Deep Learning chapter 6 OR

Deep Learning Book


Short SL project proposals

Long SL checkpoint

HW 6 (Nov 7)

Week 13: Deep learning
Nov 11 Convolutional Neural Nets/Deep learning


  Written SL short project proposals
Nov 13 Naive Bayes, Bayesian Networks

ESL Chapter 6.6

HW 7  
Week 14: Deep learning and deep RL
Nov 18

Expectation Maximization

ESL 8.5



Nov 20

Kernel methods, Kernel trick, SVR, SVMs

ESL Chapter 12
Week 15: Bayes and feature importance
Nov 25
Feature importance, learning how to interpret ML models.
    HW 7
Nov 27


Thanksgiving vacation

Week 16: Kernel methods, SVR, SVM
Dec 2 Deep RL: Emergent Tool Use from Multi-Agent Interaction

Blog post


Dec 4 Deep RL: AlphaZero AlphaZero paper    
Week 17: Final
Dec 10, 4:30-6:30
Final project writeup due