The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2014. 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 |
||||
| Aug 18 | What is machine learning? What will I learn if I take this class? Types of machine learning. | Chapter 1 (ML book) | Pretest (in class) | |
Aug 20
|
ML Introduction, Project introduction, Machine Learning that matters | ML that Matters paper | Project, HW 1 | |
Week 2 |
||||
| Aug 25 | Reinforcement learning |
Chapter 1 (Sutton & Barto) | HW 1 | |
Aug 27
|
Oral project proposals | Oral project proposals | ||
Week 3 |
||||
| Sep 1 | Labor Day (no class)
|
|||
| Sep 3 | Exploration & Exploitation, The RL Problem | 2.1-2.3, 2.5-2.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) |
Written project proposal, teams formed | |
Week 4 |
||||
| Sep 8 | The RL problem | HW 2 | ||
| Sep 10 | The RL problem | |||
Week 5 |
||||
| Sep 15 | RL problem and Dynamic Programming |
Chapter 4 (Sutton & Barto) |
Checkpoint 1 | |
| Sep 17 | Temporal Difference learning | HW 2 | ||
Week 6 |
||||
Sep 22 | MDP homework (3) in class |
6.1-6.5, 6.8 (Sutton & Barto) |
HW 3 | HW 3 |
| Sep 24 | No class
but checkpoint 2 still due |
Checkpoint 2 | ||
Week 7 |
||||
Sep 29 |
TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects |
Ch 7 (Sutton & Barto) | HW 4 | |
Oct 1 |
Introduction to supervised learning, Regression, Logistic regression, Introduction to Neural Networks |
|
||
Week 8 |
||||
| Oct 6 | Training the network, Backprop
|
Checkpoint 3 | ||
| Oct 8 | Advanced topics in neural nets including function approximation with RL and advanced network configurations, Introduction to Bayesian Networks |
HW 4 | ||
Week 9 |
||||
Oct 13 |
Neural net active learning/Homework exercise |
|
HW 5 | HW 5 |
| Oct 15 | Oral reports: Checkpoint 4 | Checkpoint 4 | ||
Week 10 |
||||
Oct 20 |
Oral reports finished
|
|
||
Oct 22 |
Using Bayes Nets for Inference, Conditional Independence, Inference |
|
||
Week 11 |
||||
| Oct 27 | Inference, Naive Bayes |
HW6 | Checkpoint 5 | |
Oct 29 |
K-means, Mixture models, Expectation Maximization (EM) | |||
Week 12 |
||||
| Nov 3 | EM for Bayesian Networks (learning structure) | HW 7 | HW 6 |
|
| Nov 5 | Boosting, Bagging, Ensemble methods | RL/NN (see RL Book 11.1) and ML book 11.9-11.13 | Checkpoint 6 | |
Week 13 |
||||
| Nov 10 | Dr McGovern was out sick |
|
||
| Nov 12 | Overfitting, Model selection, c Kernel methods, Support Vector Machines, Kernel trick, SVR |
|
HW 8 | |
Week 14 |
||||
| Nov 17 | Snow day | |||
| Nov 19 | Kernel methods, Support Vector Machines, Kernel trick, SVR (see email) |
HW 7 | ||
Week 15 |
||||
| Nov 24 | SVMs and SVR |
Preliminary writeup | ||
| Nov 26 | Thanksgiving vacation |
|||
Week 16 |
||||
| Dec 1 | PCA/Dimensionality reduction, Deep learning, Applications |
HW 8, Peer reviews | ||
| Dec 3 | Future/Applications | |||
Week 17 |
||||
| December 5, 2-5pm | CS poster session: Devon Hall atrium |
|||
| Dec 9, 4:30-6:30 | Final writeup due |
|||