Syllabus for Machine Learning 10-601 in Fall 2014
From Cohen Courses
This is the syllabus for Machine Learning 10-601 in Fall 2014.
Schedule
-| 601A | 601B | Topic | Assignment/Notes | |||||
|---|---|---|---|---|---|---|---|---|
| Wed 8/27 (Ziv) | Tues 9/2 (Wm) | Overview and Intro to Probability | ||||||
| Wed 9/3 (Ziv) | Thurs 9/4 (Wm) | Classification and K-NN | slides will be updated | |||||
| Mon 9/8 (Ziv) | Tues 9/9 (Wm) | Decision Trees, and Rule Learning | slides will be updated | |||||
| Wed 9/10 (Ziv) | Thurs 9/11 (Wm) | The Naive Bayes algorithm | ||||||
| Mon 9/15 (Ziv) | Tues 9/16 (Wm) | Linear Regression | slides will be updated | |||||
| Wed 9/17 (Ziv) | Thurs 9/18 (Wm) | Logistic Regression | ||||||
| Mon 9/22 (Wm) | Tues 9/23 (Wm) | The Perceptron algorithm | William's also lecturing in Ziv's class on Mon | |||||
| Wed 9/24 (Ziv) | Thurs 9/25 (Wm) | Neural networks and Deep Belief Networks | slides will be updated | |||||
| Mon 9/29 (Ziv) | Tues 9/30 (Ziv) | SVMs and Margin Classifiers 1 | Ziv's also lecturing in his class on Mon | |||||
| Wed 10/1 (Ziv) | Thurs 10/2 (Ziv) | SVMs and Margin Classifiers 2 | Ziv's also lecturing in his class on Wed | |||||
| Tues 10/7 | Tues 10/7 | Evaluating and Comparing Classifiers Experimentally | Ziv | William | ||||
| Thus 10/9 | Thus 10/9 | PAC Learning | Ziv | William | ||||
| Tues 10/14* | Tues 10/14* | Bias-Variance Decomposition | William's also lecturing in Ziv's class on Mon | Ziv | William | |||
| Thurs 10/16 | Thurs 10/16 | Ensemble Methods 1, Ensemble Methods 2 | slides to be updated | Ziv | William | |||
| Tues 10/21 | Tues 10/21 | Unsupervised Learning: k-Means and Mixtures | Ziv | William | ||||
| Thus 10/23 | Thus 10/23 | Unsupervised Learning: Dimensionality Reduction | Ziv | William | ||||
| Tues 10/28 | Tues 10/28 | Review session | slides to be posted | Ziv | William | |||
| Thurs 10/30 | Thurs 10/30 | Mid-term Exam | TBA: room and/or time may be different | Ziv | William | |||
| Tues 11/4* | Tues 11/4* | Graphical Models 1 | Ziv's also lecturing in his class on Mon | Ziv | William | |||
| Thurs 11/6* | Thurs 11/6* | Graphical Models 2 | Ziv's also lecturing in his class on Wed | Ziv | William | |||
| Tues 11/11* | Tues 11/11* | HMMS and Sequences | Ziv's also lecturing in his class on Mon | Ziv | William | |||
| Thus 11/13* | Thus 11/13* | Matrix Factorization and Topic Models | William's also lecturing in Ziv's class on Wed, slides to be updated | Ziv | William | |||
| Tues 11/18* | Tues 11/18* | Network Models | William's also lecturing in Ziv's class on Mon | Ziv | William | |||
| Thurs 11/20* | Thurs 11/20* | Semi-supervised learning | William's also lecturing in Ziv's class on Wed | Ziv | William | |||
| Tues 11/25* | Tues 11/25* | Scalable Learning and Parallelization | William's also lecturing in Ziv's class on Mon | Ziv | William | |||
| Thurs 11/27 | Thurs 11/27 | No class - Thanksgiving | ||||||
| Tues 12/2* | Tues 12/2* | Learning and NLP | Ziv | William | ||||
| Thurs 12/4 | Thurs 12/4 | Learning and Biology | Ziv | William |
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.
