CS 4033/5033 Machine Learning Class schedule

Fall 2016

The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2016. This specific order of each topic will depend on the class projects and interest. This will be finalized in the first week. 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 22 What is machine learning? What will I learn if I take this class? Types of machine learning. Overview of the course. Elements of Statistical Learning (ESL), Chapter 1   Pretest (in class)

Aug 24


ML Introduction, Project introduction, Machine Learning that matters ML that Matters paper Project, HW 1  
Week 2: Project and Nearest Neighbor and Regression methods
Aug 29

Introduction to SL, Least Squares, Linear Regression, Nearest Neighbor, K-means clustering

ESL Chapter 2.1-2.3, ESL Chapter 3.1-3.2   HW 1

Aug 31


Oral project proposals     Oral project proposals
Week 3: Regression methods
Sep 5
Labor Day (no class)
Sep 7

Nearest neighbor, K-means clustering

Ridge, Lasso, and Elastic nets

Logistic regression

Start of overfitting

Ridge/Lasso/Elastic: ESL Chapter 3.3-3.8

Logistic: Wikipedia and ESL Chapter 4.4


HW 2 Written project proposal, teams formed
Week 4: Neural nets and Deep learning
Sep 12

Overfitting, Bias-Variance Tradeoff, Model selection, Model evaluation, Introduction to Neural Networks

ESL Chapter 7

Sep 14

Neural Networks, Training the network

ESL Chapter 11, Mitchell Ch 4   HW 2 (due Sep 16)
Week 5: Neural nets and Deep learning
Sep 19

Training the network/Backpropagation, examples



Checkpoint 1

Sep 21 Neural net active learning/In-class homework exercise   HW 3 HW 3
Week 6: Model evaluation

Sep 26

Convolutional Neural Nets/Deep learning

Neural Networks and Deep Learning chapter 6 and see the readings on Canvas

HW 4


Sep 28

Convolutional Neural Nets/Deep learning

    Checkpoint 2
Week 7: Tree-based methods

Oct 3

Decision trees

ESL Chapter 9.2, Mitchell book chapter 3


Oct 5

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


ESL Chapter 8.7, ESL Chapter 10, ESL Chapter 15  

HW 4

Week 8: Reinforcment Learning
Oct 10 Guest lecture: Dan Sheldon (Departmental colloquium)   HW 5 Checkpoint 3
Oct 12

Boosting, Bagging, Ensemble methods, Random Forests and Gradient Boosted Regression Trees, Introduction to Reinforcement learning

Sutton & Barto: Chapter 1

Week 9: Reinforcment Learning

Oct 17

Exploration & Exploitation, The RL Problem

Sutton & Barto: 2.1-2.3, 2.5-2.7



Oct 19

The RL Problem and Temporal Difference learning

Sutton & Barto: Chapter 3

  HW 5 (Oct 21)
Week 10: Project reports

Oct 24

Oral reports: Checkpoint 4


  Checkpoint 4

Oct 26

Oral reports: Checkpoint 4


Checkpoint 4

Week 11: Reinforcement Learning
Oct 31

The RL Problem and Temporal Difference learning


Nov 2

MDP homework in class   HW 6 HW 6
Week 12: Support Vector Machines and Kernel Methods
Nov 7

TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects

Sutton & Barto: Chapter 6-7  

Checkpoint 5

Nov 9 Advanced RL as needed for projects, Advanced topics in neural nets including function approximation with RL RL/NN (see RL Book 11.1) and ML book 11.9-11.13 HW 7  
Week 13: Graphical Models
Nov 14 Kernel methods, Support Vector Machines, Kernel trick, SVR, SVMs

ESL Chapter 11

Nov 16 SVMs continued


  Checkpoint 6
Week 14: Graphical Models
Nov 21 Introduction to Bayesian Networks Mitchell NEW Chapter 3 (old chapter 6 but it is updated)   HW 7
Nov 23
Thanksgiving vacation
Week 15: EM and graphical models
Nov 28
Inference, Naive Bayes
HW 8 Preliminary writeup
Nov 30
Using Bayes Nets for Inference, Conditional Independence, Inference
Using Bayes Nets for Inference, Conditional Independence, Inference   HW 8
Week 16: Future of ML
Dec 5
Finish Bayes, Project summaries
    Peer reviews
Dec 7 Project summaries      
Week 17: Final
Dec 12, 4:30-6:30
Final writeup due