Assignments are due on the day on which they are listed.
| Date | Reading | Topic | Assignments Due |
|---|---|---|---|
| Aug 22 | Intro and Motivation | Read syllabus/schedule | |
| Aug 24 | 1, 2.1, 3.1-3.3 | Perceptron and Delta Rule | Install toolkit (before class) |
| Aug 29 | 3.4 | Perceptron and Delta Rule | Perceptron HW (before class) |
| Aug 31 | 2.2 Perceptron Lab description | Data, Testing, and ML Toolkit | Quadric Machine HW (before class) |
| Sep 5 | 3.5 Logistic Regression | Linear Regression and Logistic Regression | |
| Sep 7 | 2.4-2.5 Group Project proposal description | Inductive Bias | Linear Regression HW (before class) Logistic Regression HW (before class) |
| Sep 12 | 4.1-4.3 | Backpropagation | Perceptron Lab (due by 11 pm) |
| Sep 14 | 4.4-4.5 Experiment with TensorFlow playground Backpropagation Lab description | Backpropagation | Backpropagation HW (before class) |
| Sep 19 | 4.6 | Backpropagation | |
| Sep 21 | 2.2 (review) | Comparing Classifiers, Grad School | Group Project Proposal |
| Sep 26 | 6.1 Optional: Data Preparation | Feature Selection | |
| Sep 28 | 6.2,6.5-6.6 Optional: 6.3-6.4 | Feature Reduction (PCA) | Group Project Voting (due by 11 pm) |
| Oct 3 | 12.1-12.2 | Decision Trees | Backpropagation Lab (pushed to 10/5, due by 11 pm) Begin gathering data for group project |
| Oct 5 | Decision Trees | Data gathering email report (due by 11 pm) | |
| Oct 10 | 12.3-12.4 Decision Tree Lab description | Decision Trees | Decision Tree HW (before class) |
| Oct 12 | Midterm (must start exam by 9pm) | ||
| Oct 17 | 7.2 Nearest Neighbor Lab description | Nearest Neighbor | |
| Oct 19 | 2.3 | Bayesian Learning | k-Nearest Neighbor HW (before class) |
| Oct 24 | 13 | Ensembles | Naïve Bayes HW (before class) |
| Oct 26 | 14.1 Section 5.4 of this reading | Clustering and Unsupervised Learning | Decision Tree Lab (due by 11 pm) Group Project Progress Report |
| Oct 31 | 14.2-14.3 Clustering Lab description | Clustering and Unsupervised Learning | HAC HW (before class) |
| Nov 2 | 10.1-10.4 | Genetic Algorithms | Nearest Neighbor Lab (due by 11 pm) k-Means HW (before class) |
| Nov 7 | 11.1-11.3 | Markov Models | |
| Nov 9 | Supercomputing with Linux | ||
| Nov 14 | 11.4-11.7 | Reinforcement Learning and Q-Learning | Clustering Lab (due by 11 pm) |
| Nov 16 | Case Study #3: Optimizing Schools Additional Optional Reading Fair and Optimal Prediction via Post-Processing | Ethics of Machine Learning | RL HW (before class) |
| Nov 21 | FALL RECESS | ||
| Nov 23 | FALL RECESS | ||
| Nov 28 | 8.1-8.3 | SVMs | Email to Mr. Vulcani |
| Nov 30 | Final Exam Review | ||
| Dec 5 | Group Project Oral Presentations | ||
| Dec 7 | Group Project Oral Presentations | Group Project report (due by 11 pm) | |
| Dec 14 | Final Exam (7:30am) |
The schedule is subject to change.
