Assignments are due on the day on which they are listed.
| Date | Reading | Topic | Assignments Due |
|---|---|---|---|
| Aug 18 | Intro and Motivation | Read syllabus/schedule | |
| Aug 20 | 1, 2.1, 3.1-3.3 | Perceptron and Delta Rule | Install toolkit (before class) |
| Aug 25 | 3.4 | Perceptron and Delta Rule | Perceptron HW (before class) |
| Aug 27 | 2.2 Perceptron Lab description | Data, Testing, and ML Toolkit | Quadric Machine HW (before class) |
| Sep 1 | 3.5 Logistic Regression | Linear Regression and Logistic Regression | |
| Sep 3 | 2.4-2.5 Group Project proposal description | Inductive Bias | Linear Regression HW (before class) Logistic Regression HW (before class) |
| Sep 8 | 4.1-4.3 | Backpropagation | Perceptron Lab (due by 11 pm) Service-learning proposal (due by 11 pm) |
| Sep 10 | 4.4-4.5 Experiment with TensorFlow playground Backpropagation Lab description | Backpropagation | Backpropagation HW (before class) |
| Sep 15 | 4.6 | Backpropagation | |
| Sep 17 | 2.2 | Comparing Classifiers, Grad School | Group Project Proposal |
| Sep 22 | 6.1 Optional: Data Preparation | Feature Selection | |
| Sep 24 | 6.2,6.5-6.6 Optional: 6.3-6.4 | Feature Reduction (PCA) | Group Project Voting |
| Sep 29 | 12.1-12.2 | Decision Trees | Backpropagation Lab (due by 11 pm) Begin gathering data for group project |
| Oct 1 | 12.3-12.4 Decision Tree Lab description | Decision Trees | Decision Tree HW (before class) Data gathering report |
| Oct 6 | Midterm | ||
| Oct 8 | 5.1-5.2,7-7.2.1 Nearest Neighbor Lab description | Nearest Neighbor | |
| Oct 13 | 2.3 | Bayesian Learning | k-Nearest Neighbor HW (before class) |
| Oct 15 | 7.2.2-7.2.3 Optional: 5.3 | Radial Basis Function Networks, Data Mining | Naïve Bayes HW (before class) |
| Oct 20 | 13 | Ensembles | Decision Tree Lab (due by 11 pm) |
| Oct 22 | 14.1 Section 5.4 of this reading | Clustering and Unsupervised Learning | Group Project Progress Report |
| Oct 27 | 14.2-14.3 Clustering Lab description | Clustering and Unsupervised Learning | HAC HW (before class) |
| Oct 29 | 10.1-10.4 | Supercomputing with Linux | Nearest Neighbor Lab (due by 11 pm) k-Means HW (before class) |
| Nov 3 | 10.1-10.4 | Genetic Algorithms | |
| Nov 5 | Group Project Time | ||
| Nov 10 | 11.4-11.7 | Reinforcement Learning and Q-Learning | Clustering Lab (due by 11 pm) |
| Nov 12 | Case Study #3: Optimizing Schools Additional Optional Reading | Ethics of Machine Learning | RL HW (before class) |
| Nov 17 | 8.1-8.3 | SVMs | Email to Mr. Vulcani (by thurs is fine, too) |
| Nov 19 | Final Exam Review | Group Project Report (due by 11 pm) | |
| Nov 24 | Group Project Oral Presentations:
| Service-Learning final report (due Dec 4, by 11 pm) | |
| Nov 26 | FALL RECESS | ||
| Dec 1 | Final available all day |
The schedule is subject to change. The final is Tuesday, Dec 1, via Examity on Moodle.
