Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"
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| Mon 11/10 (Ziv) || Tues 11/11 ('''Ziv''') || [[10-601 HMMs|HMMs - learning]] || || HW7: HMMS and Graphical Models (worksheet) - due 11/17. Kuo Liu and Harry Gifford | | Mon 11/10 (Ziv) || Tues 11/11 ('''Ziv''') || [[10-601 HMMs|HMMs - learning]] || || HW7: HMMS and Graphical Models (worksheet) - due 11/17. Kuo Liu and Harry Gifford | ||
|- | |- | ||
− | | Wed 11/12 ('''Wm''') || Thur 11/13 (Wm) || [[10-601 Matrix Factorization|PCA and Matrix Factorization]]|| ''slides to be updated'' || [http://www.cs.cmu.edu/~wcohen/10-601/project-2014/project | + | | Wed 11/12 ('''Wm''') || Thur 11/13 (Wm) || [[10-601 Matrix Factorization|PCA and Matrix Factorization]]|| ''slides to be updated'' || [http://www.cs.cmu.edu/~wcohen/10-601/project-2014/project.pdf Project Instructions] |
|- | |- | ||
− | | Mon 11/17 ('''Wm''') || Tues 11/18 (Wm) || [[10-601 | + | | Mon 11/17 ('''Wm''') || Tues 11/18 (Wm) || [[10-601 Topic Models| 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)|| [[10-601 Semi-supervised learning|Semi-Supervised Learning]] || || | | Wed 11/19 ('''Wm''') || Thur 11/20 (Wm)|| [[10-601 Semi-supervised learning|Semi-Supervised Learning]] || || | ||
|- | |- | ||
− | | Mon 11/24 ('''Wm''') || Tues 11/25 (Wm) || [[10-601 Big Data|Scalable Learning and Parallelization]] || || | + | | Mon 11/24 ('''Wm''') || Tues 11/25 (Wm) || [[10-601 Big Data|Scalable Learning and Parallelization]] || || [http://www.cs.cmu.edu/~wcohen/10-601/project-2014/milestone1.html Submission process for MS1 Writeup] |
|- | |- | ||
| Wed 11/26 || Thur 11/27 || || ''No class - Thanksgiving'' || | | Wed 11/26 || Thur 11/27 || || ''No class - Thanksgiving'' || | ||
|- | |- | ||
− | | Mon 12/1 ('''Wm''') || Tues 12/2 (Wm) || Learning and NLP || | + | | Mon 12/1 ('''Wm''') || Tues 12/2 (Wm) || [[10-601 Structured Output Learning and NLP|Learning and NLP]] || || Project milestone 1 (Test one classifier per team member on the test data) |
|- | |- | ||
− | | Wed 12/3 (Ziv) || Thurs 12/4 ('''Ziv''') || | + | | Wed 12/3 (Ziv) || Thurs 12/4 ('''Ziv''') || [[10-601 HMM in biology|HMM applications in biology]] || || |
|- | |- | ||
| Wed 12/10|| || || || Project due (Final experiments and writeup) | | Wed 12/10|| || || || Project due (Final experiments and writeup) | ||
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| 11/11-11/13 || Kuo Liu and Harry Glifford || Bayesian Networks and HMMs || [http://www.andrew.cmu.edu/user/dbiswas/class/10601/recitation/recitation7/recitation7.pdf HMMs] | | 11/11-11/13 || Kuo Liu and Harry Glifford || Bayesian Networks and HMMs || [http://www.andrew.cmu.edu/user/dbiswas/class/10601/recitation/recitation7/recitation7.pdf HMMs] | ||
[http://curtis.ml.cmu.edu/w/courses/images/6/6d/Gm.pdf Graphical Models] | [http://curtis.ml.cmu.edu/w/courses/images/6/6d/Gm.pdf Graphical Models] | ||
+ | |- | ||
+ | | 11/18-11/20 || Kuo Liu and Yipei Wang || SVD PCA CF LDA || [https://www.dropbox.com/s/vdjag3yvx4brdu2/recitation8.ppt?dl=0 Matrix Factorization and Topic Model] | ||
|} | |} | ||
Latest revision as of 10:37, 3 December 2014
This 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.