Syllabus for Machine Learning 10-601 in Fall 2014

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This is the syllabus for Machine Learning 10-601 in Fall 2014.

Lecture Schedule

Schedule for 10-601 in Fall 2014
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

Schedule for Recitations for 10-601 in Fall 2014
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

Graphical Models

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.