Syllabus for Machine Learning 10-601 in Fall 2014
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Jump to navigationJump to searchThis is the syllabus for Machine Learning 10-601 in Fall 2014.
Lecture Schedule
| Lecture for 601-A | Lecture for 601-B | Topic | Notes | Assignment |
|---|---|---|---|---|
| Wed 8/27 (Ziv) | Tues 9/2 (Wm) | Course Overview and Introduction to Probability | HW0: Self-assessment test. This need not be turned in for a grade. | |
| Wed 9/3 (Ziv) | Thur 9/4 (Wm) | Classification and K-NN | ||
| Mon 9/8 (Ziv) | Tues 9/9 (Wm) | Decision Trees and Rule Learning | ||
| Wed 9/10 (Ziv) | Thur 9/11 (Wm) | The Naive Bayes algorithm | HW1: KNN and Decision Trees HW1 Solutions - due 9/18. Jingwei Shen and Abhinav Maurya | |
| Mon 9/15 (Ziv) | Tues 9/16 (Wm) | Linear Regression | ||
| Wed 9/17 (Ziv) | Thur 9/18 (Wm) | Logistic Regression | HW2: Naive Bayes, Linear Regression (Matlab Programming) , Solutions - due 9/25. Siddhartha Jain and Ying Yang | |
| Mon 9/22 (Wm) | Tues 9/23 (Wm) | The Perceptron algorithm | ||
| Wed 9/24 (Ziv) | Thur 9/25 (Wm) | Neural networks and Deep Belief Networks | HW3: Logistic Regression, Neural networks (Matlab Programming) - due 10/9. Jin Sun and Harry Gifford | |
| Mon 9/29 (Ziv) | Tues 9/30 (Ziv) | SVMs and Margin Classifiers | ||
| Wed 10/1 (Ziv) | Thur 10/2 (Ziv) | SVMs: Duality and kernels | ||
| Mon 10/6 (Ziv) | Tues 10/7 (Wm) | Evaluating and Comparing Classifiers Experimentally | ||
| Wed 10/8 (Ziv) | Thus 10/9 (Wm) | PAC Learning | HW4: SVM, Comparing classifiers (Experiments) - due 10/18. Qihui (Anna) Li and Siping Ji | |
| Mon 10/13 (Dalvi, k-means) | Tues 10/14 (Ziv, agglomerative+spectral) | Clustering | ||
| Wed 10/15 (Ziv, agglomerative+spectral) | Thur 10/16 (Dalvi, k-means) | Clustering | HW5: Pac-learning (worksheet)- due 10/25. Ying Yang and Abhinav Maurya | |
| Mon 10/20 (Wm) | Tues 10/21 (Wm) | Bias-Variance Decomposition | ||
| Wed 10/22 (Ziv) | Thur 10/23 (Wm) | Ensemble Methods | ||
| Mon 10/27 (Ziv) | Tues 10/28 (Wm) | 10-601 Fall 2014 Review Session | ||
| Wed 10/29 7pm DH 2210 | Wed 10/29 7pm DH 2315 | Mid-term Exam | The midterm for both sections is 7-9pm 10/29, and there's no class Thursday 10/30. | Old midterm links Current midterm solution |
| Mon 11/3 (Ziv) | Tues 11/4 (Ziv) | Bayesian networks | HW6: unsupervised learning (programming) - due 11/10. Jingwei Shen and Daniel Ribeiro Silva | |
| Wed 11/5 (Ziv) | Thur 11/6 (Ziv) | HMMs - inference | ||
| Mon 11/10 (Ziv) | Tues 11/11 (Ziv) | HMMs - learning | HW7: HMMS and Graphical Models (worksheet) - due 11/17. Kuo Liu and Harry Gifford | |
| Wed 11/12 (Wm) | Thur 11/13 (Wm) | PCA and Matrix Factorization | slides to be updated | Project Instructions |
| Mon 11/17 (Wm) | Tues 11/18 (Wm) | LDA and Network Models | HW8: Topic models (worksheet, experiments with PCA code) - due 11/24. Kuo Liu and Yipei Wang. | |
| Wed 11/19 (Wm) | Thur 11/20 (Wm) | Semi-Supervised Learning | ||
| Mon 11/24 (Wm) | Tues 11/25 (Wm) | Scalable Learning and Parallelization | Submission process for MS1 Writeup | |
| Wed 11/26 | Thur 11/27 | No class - Thanksgiving | ||
| Mon 12/1 (Wm) | Tues 12/2 (Wm) | Learning and NLP | Project milestone 1 (Test one classifier per team member on the test data) | |
| Wed 12/3 (Ziv) | Thurs 12/4 (Ziv) | HMM applications in biology | ||
| Wed 12/10 | Project due (Final experiments and writeup) |
Recitation Schedule
| Recitation Date | TAs | Topic | Notes |
|---|---|---|---|
| 09/08-09/10 | Yipei Wang and Qihui Li | Matlab Introduction | Slides, e1.m, plot_example.m |
| 09/15-09/17 | Jingwei Shen and Abhinav Maurya | Probability, MLE, KNN, Decision Trees | Probability & MLE, Entropy, Decision Trees, KNN |
| 09/22-09/25 | Sid Jain and Ying Yang | Naive Bayes and linear regression | |
| 09/29-10/03 | Jin Sun and Henry (Harry) Gifford | Math Review | Math Review |
| 09/29-10/03 | Jin Sun and Henry (Harry) Gifford | Logistic Regression and Neural Networks | LR and NN slides |
| 10/13-10/17 | Qihui Li and Daniel Ribeiro Silva | SVM and Compare Classifier | SVM and Compare Classifiers |
| 10/20-10/22 | Ying Yang and Abhinav Maurya | PAC-learning and clustering | PAC-learning K-Means and GMM Clustering |
| 11/11-11/13 | Kuo Liu and Harry Glifford | Bayesian Networks and HMMs | HMMs |
| 11/18-11/20 | Kuo Liu and Yipei Wang | SVD PCA CF LDA | Matrix Factorization and Topic Model |
Other duties:
- Debjani Biswas, Autolab master
- Daniel Ribeiro Silva, Autolab master, assignments 1-4.
- Jin Sun, Piazza monitoring: alerting Ziv and William if there are issues and tracking TA contributions
- Yipei Wang and Qihui (Anna) Li: Matlab
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.
