| Date | Reading | Topic | Assignments Due |
|---|---|---|---|
| Aug 20 | Intro and Motivation | Read syllabus/schedule | |
| Aug 22 | 1, 2.1, 3.1-3.3 | Perceptron and Delta Rule | Install toolkit (before class) |
| Aug 27 | 3.4 | Perceptron and Delta Rule | Perceptron HW (before class) |
| Aug 29 | 2.2 Perceptron Lab description | Data, Testing, and ML Toolkit | Quadric Machine HW (before class) |
| Sep 3 | 3.5 Logistic Regression | Linear Regression and Logistic Regression | |
| Sep 5 | 2.4-2.5 Group Project proposal description | Inductive Bias | Linear Regression HW (before class) Logistic Regression HW (before class) |
| Sep 10 | 4.1-4.3 | Backpropagation | Perceptron Lab (due by 11 pm) Service-learning proposal (due by 11 pm) |
| Sep 12 | 4.4-4.5 Experiment with TensorFlow playground Backpropagation Lab description | Backpropagation | Backpropagation HW (before class) |
| Sep 17 | 4.6 | Backpropagation | |
| Sep 19 | 2.2 | Comparing Classifiers, Grad School | Group Project Proposal |
| Sep 24 | 6.1 Optional: Data Preparation | Feature Selection and Reduction | |
| Sep 26 | 6.2,6.5-6.6 Optional: 6.3-6.4 | Features (cont), PCA | Group Project Voting (due by 11 pm on 9/27) |
| Oct 1 | 12.1-12.2 | Decision Trees | Backpropagation Lab (due by 11 pm) Begin gathering data for group project |
| Oct 3 | 12.3-12.4 Decision Tree Lab description | Decision Trees | Decision Tree HW (before class) Data gathering email report |
| Oct 8 | Midterm | ||
| Oct 10 | 5.1-5.2,7-7.2.1 Nearest Neighbor Lab description | Nearest Neighbor (Lecture online - No Class) | |
| Oct 15 | 2.3 | Bayesian Learning | k-Nearest Neighbor HW (before class) |
| Oct 17 | 7.2.2-7.2.3 Optional: 5.3 | Radial Basis Function Networks, Data Mining | Naïve Bayes HW (before class) |
| Oct 22 | 13 | Ensembles | Decision Tree Lab (due by 11 pm) |
| Oct 24 | 14.1 Section 5.4 of this reading | Clustering and Unsupervised Learning | Group Project Progress Report |
| Oct 29 | Case Studies Clustering Lab description | Ethics and the Future of Machine Learning | HAC HW (before class) |
| Oct 31 | 10.1-10.4 | Genetic Algorithms | Nearest Neighbor Lab (due by 11 pm) k-Means HW (before class) |
| Nov 5 | 11.1-11.3 | Markov Models | |
| Nov 7 | 16.3 | Hidden Markov Models | |
| Nov 12 | 11.4-11.7 | Reinforcement Learning and Q-Learning | Clustering Lab (due by 11 pm) |
| Nov 14 | Group Project Time | RL HW (before class) | |
| Nov 19 | 8.1-8.3 | SVMs | |
| Nov 21 | Final Exam Review | ||
| Nov 26 | FALL RECESS | ||
| Nov 28 | FALL RECESS | ||
| Dec 3 | Group Project Oral Presentations:
| ||
| Dec 5 | Group Project Oral Presentations:
| Service-Learning report (due by 11 pm) Group Project report (due by 11 pm) | |
| Dec 10 | Final (7:30am) |
The schedule is subject to change. The final is Tuesday, Dec 10, 7:30-9:30 a.m. in our normal classroom.
