Schedule
This is a tentative syllabus and schedule. Topics, reading assignments, due dates, and exam dates are subject to change. All assignments and projects are due by 11:59:59pm Eastern time on the day listed.
The readings will come from Machine Learning (Flach), Learning from Data (LfD), the reading packet (Handout), or online sources.
Recordings of the lectures are available online via Canvas.
| Week | Date | Topic | Recitation | Assignments | Readings |
|---|---|---|---|---|---|
| 1 | Thu 1/11 | Lecture #0: Introduction to CIS 419/519[pptx][pdf][video] | Sign up for Piazza | A Machine Learning Puzzle | |
| 2 | Tue 1/16 | Lecture #1: Introduction to Machine Learning[pptx][pdf][video] | Recitation #1: Python Session - 1 [pptx][pdf] | ||
| Thu 1/18 | Lecture #1: Introduction to Machine Learning[pptx][pdf][video] | ||||
| 3 | Tue 1/23 | Lecture #2: Decision Trees; Over-fitting [pptx][pdf][video] | Recitation #2: Python Session - 2 [pptx][pdf] | ||
| Thu 1/25 | Lecture #2: Decision Trees; Over-fitting[pptx][pdf][video] | Quiz 1 out (due on 1/28) [sol] HW 1 out (due on 2/9) |
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| Mon 1/29 | Course Selection Period Ends | ||||
| 4 | Tue 1/30 | Lecture #3: Evaluation[pptx][pdf][video] | Recitation #3 [pptx][pdf] | ||
| Thu 2/1 | Lecture # 4: On-line Learning, Perceptron, Kernels[pptx][pdf][video] | Quiz 2 out (due on 2/5) [sol] | |||
| 5 | Tue 2/6 | No Class | Recitation #4 [GitHub][ipynb] | ||
| Thu 2/8 | No Class | Quiz 3 out (due on 2/14) [sol] | |||
| 6 | Tue 2/13 | Lecture # 4: On-line Learning, Perceptron, Kernels [video] | Recitation #5 [GitHub][ipynb] | ||
| Thu 2/15 | Lecture # 4: On-line Learning, Perceptron [video] | Quiz 4 out (due on 2/19) [sol] HW 2 out (due on 2/26) |
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| Fri 2/16 | Drop Period Ends | ||||
| 7 | Tue 2/20 | Lecture #5: Why Machine Learning Works: Explaining Generalization [pptx][pdf][video] | Recitation #6 [pdf] | ||
| Thu 2/22 | Lecture #5: Why Machine Learning Works: Explaining Generalization [video] | ||||
| 8 | Tue 2/27 | Lecture #5: Why Machine Learning Works: Explaining Generalization [video] | |||
| Thu 3/1 | Midterm Exam | [Solution] | [CIS519 Midterm Fall16], [CIS519 Midterm Fall17], [CS446 Midterm Spring17] | ||
| 9 | 3/03 - 3/11 | Spring Break | |||
| 10 | Tue 3/13 | Lecture #6: Support Vector Machine [pptx][pdf][video] | |||
| Thu 3/15 | Lecture #7: Boosting and Ensembles ; Multi-class Classification and Ranking [pptx][pdf][video] | Quiz 5 out (due on 3/19) [sol] HW 3 out (due on 3/30) |
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| 11 | Tue 3/20 | Lecture #8: Neural Networks and Deep Learning [pptx][pdf][video] | Recitation #7 | ||
| Thu 3/22 | Lecture #8: Neural Networks and Deep Learning [video] | Quiz 6 out (due on 3/26) [sol] | |||
| 12 | Tue 3/27 | Lecture #9: Generative Models; Naive Bayes [pptx][pdf][video] | |||
| Thu 3/29 | Lecture #9: Generative Models; Naive Bayes [video] | Quiz 7 out (due on 4/2) [sol] |
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| Tue 3/30 | Last day to withdraw | ||||
| 13 | Tue 4/03 | Lecture #10: Un/Semi-Supervised Learning: EM and K-Means [pptx][pdf][video] | Recitation #9[pdf] | HW 4 out (due on 4/16) | |
| Thu 4/05 | Lecture #10: Un/Semi-Supervised Learning: EM and K-Means [video] | Quiz 8 out (due on 4/9) [sol] | |||
| 14 | Tue 4/10 | Lecture #11: Bayesian Networks [pptx] [pdf] [video] | |||
| Thu 4/12 | Lecture #11: Bayesian Networks [video] | ||||
| 15 | Tue 4/17 | Lecture #12: Clustering / Dimensionality Reduction [pptx] [pdf] [video] | HW 5 out (due on 4/26) | ||
| Thu 4/19 | Lecture #12: Clustering / Dimensionality Reduction [video] | ||||
| 16 | Tue 4/24 | Final Comments and Review [video] | Quiz 9 out (due on 04/30) [sol] | ||
| 17 | Mon 4/30 | Final exam (9am; Chem 102) | Appendix | [CS446 Final 2012], [CS446 Final 2012 Solutions], [CS446 Final 2016], [CS446 Final 2016 Solutions] | |
| Mon 5/07 | Final Projects Poster Session (6pm Active Learning Room, 3401 Walnut) | ||||