Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
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''Teaching team: also see the [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]'' | ''Teaching team: also see the [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]'' | ||
Revision as of 17:08, 6 January 2016
This is the syllabus for Machine Learning 10-601 in Spring 2016.
Schedule
Teaching team: also see the Google Doc Spreadsheet
| Date | Main Topic of Lecture | Lecturer | Assignment | TAs |
|---|---|---|---|---|
| M 1/11 | Course Overview | Nina | ||
| W 1/13 | Intro to Probability | William | HW1 Probabilities | Will, Han |
| M 1/18 | Martin Luther King Day | |||
| W 1/20 | The Naive Bayes algorithm | William | HW2: implementing naive Bayes | Travis, Maria |
| M 1/25 | Logistic Regression | William | ||
| W 1/27 | Perceptrons and SVMs | William | ||
| M 2/1 | Kernels | Nina | ||
| W 2/3 | Linear Regression | Nina | HW3: implementing logistic regression and kernel perceptrons | Tianshu, Will |
| M 2/8 | Neural Networks and Backprop | Nina | ||
| W 2/10 | Decision Trees and Rules | Nina | ||
| M 2/15 | Boosting and Other Ensembles | Nina | ||
| W 2/17 | Theory 1 | Nina | HW4: Theory | Han, Tianshu |
| M 2/22 | Theory 2 | Nina | ||
| W 2/24 | Midterm Review | Nina | ||
| M 2/29 | Midterm exam | |||
| W 3/2 | Unsupervised Learning: k-Means and Mixtures | Nina | ||
| M 3/7 | Semi-Supervised Learning | Nina | ||
| W 3/9 | Active Learning | Nina | HW5: Active learning and clustering | Travis, Han |
| M 3/14 | Spring break | |||
| W 3/16 | Spring break | |||
| M 3/21 | Graphical Models 1 | William | ||
| W 3/23 | Graphical Models 2 | William | ||
| M 3/28 | Graphical Models for Sequential Data | William | HW6: Graphical models | Maria, Renato |
| M 4/4 | Topic Models | William | ||
| W 4/6 | PCA and dimension reduction | William | ||
| M 4/11 | Matrix Factorization and collaborative filtering | William | ||
| W 4/13 | Deep Learning 1 | William | ||
| M 4/18 | Deep Learning 2 | William | ||
| W 4/20 | Reinforcement Learning | Nina | ||
| M 4/22 | Review | Nina and William | ||
| W 4/24 | Final exam | |||
To other instructors: if you'd like to use any of the materials found here, you're absolutely welcome to do so, but please acknowledge their ultimate source somewhere.
