COMS W4721 Machine Learning for Data Science

Columbia University, Spring 2017


Instructor: John Paisley
Location: 501 Schermerhorn Hall
Time: T/Th 7:40pm - 8:55pm
Office hours: Monday 11am-12pm @ 422 Mudd Building

TA's:
Ghazal Fazelnia
gf2293@columbia.eduCVN office hours via email (no fixed time)

Tianhao Lu
tl2710@columbia.edu
Tue/Thu 1pm - 2pm @ CS TA room, Mudd 122A (1st floor)

Dheeraj Kalmekolan
drk2143@columbia.edu
Wed 4:30pm - 6:30pm @ CS TA room, Mudd 122A (1st floor)

Ashutosh Nanda
an2655@columbia.edu
Fri 1:30pm - 3:30pm @ CS TA room, Mudd 122A (1st floor)

Avinash Bukkittu
ab4377@columbia.edu
Tues 10am - 12pm @ CS TA room, Mudd 122A (1st floor)

Yuhao Zhang
yz3044@columbia.edu
Tues 9pm - 11pm @ CS TA room, Mudd 122A (1st floor)

Jiefu Ying
jy2799@columbia.edu
Fri 4pm - 6pm @ CS TA room, Mudd 122A (1st floor)

George Yu
gy2206@columbia.edu Tues 4pm - 5pm & Wed 12pm-1pm @ CS TA room, Mudd 122A (1st floor)

Peng Wu
pw2393@columbia.edu
Mon 6:30pm - 8:30pm @ CS TA room, Mudd 122A (1st floor)

Synopsis:   This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models. Methods covered in class include linear and logistic regression, support vector machines, boosting, K-means clustering, mixture models, expectation-maximization algorithm, hidden Markov models, among others. We will cover algorithmic techniques for optimization, such as gradient and coordinate descent methods, as the need arises.

Prerequisites:   Basic linear algebra and calculus, introductory-level courses in probability and statistics. Comfort with a programming language (e.g., Matlab) will be essential for completing the homework assignments. Not open to students who have taken COMS 4771, STATS 4400 or IEOR 4525.

Text:   There is no required text for the course. Suggested readings for each class will be given from the textbooks below. These readings are meant to be general pointers and may contain more material than we cover in class.

    T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Second Edition, Springer. [link]
    C. Bishop, Pattern Recognition and Machine Learning, Springer. [link]
    H. Daume, A Course in Machine Learning, Draft. [link]

Grading:   5 homework assignments (50%), midterm exam (25%), final in-class exam (25%). Each homework assignment will have a programming component that will count significantly toward the final homework grade. The final in-class exam will focus on material from the second half of the course.


Date
Slides
Topics covered
Suggested readings
Additional Information
Week 1
1/17/2017
[PDF]
Introduction, maximum likelihood estimation ESL Ch. 1-2; PRML Ch. 2.1-2.3

1/19/2017
[PDF] linear regression, least squares, geometric view ESL Ch. 3.1-3.2; PRML Ch. 1.1, 3.1
Week 2
1/24/2017
[PDF] ridge regression, probabilistic views of linear regression ESL Ch. 3.3-3.4; PRML Ch. 3.1-3.2 Homework 1 out (see Courseworks)

1/26/2017
[PDF] bias-variance, Bayes rule, maximum a posteriori ESL Ch. 7.1-7.3, 7.10; PRML Ch 2.3 Due February 5 by 11:59pm
Week 3
1/31/2017
[PDF] Bayesian linear regression PRML 3.3-3.5

2/2/2017
[PDF] sparsity, subset selection for linear regression ESL Ch. 3.3-3.8
Week 4
2/7/2017
[PDF]
nearest neighbor classification, Bayes classifiers ESL Ch. 13.3-13.5; CML Ch. 2, 7

2/9/2017

No class (University-wide)


Week 5
2/14/2017
[PDF]
linear classifiers, perceptron ESL Ch. 4.5; CML 3 Homework 2 out (see Courseworks)

2/16/2017
[PDF] logistic regression, Laplace approximation ESL Ch. 4.4; PRML Ch. 4.3-4.5 Due February 26 by 11:59pm
Week 6
2/21/2017
[PDF] kernel methods, Gaussian processes ESL Ch. 6; PRML Ch. 6; CML Ch. 9

2/23/2017
[PDF] maximum margin, support vector machines ESL Ch. 12.1-12.3; PRML Ch. 7.1
Week 7
2/28/2017
[PDF] trees, random forests ESL Ch. 9.2, 15; CML Ch. 1

3/2/2017
[PDF] boosting ESL Ch. 10; CML Ch. 11
Week 8
3/7/2017

Midterm exam (covers material through Week 7)



3/9/2017

no class


Week 9
3/14/2017

no class (Spring break)

Homework 3 out (see Courseworks)

3/16/2017

no class (Spring break)

Due March 26 by 11:59pm
Week 10
3/21/2017
[PDF]
clustering, k-means ESL Ch. 14.3;  PRML Ch. 9.1; CML Ch. 13

3/23/2017
[PDF] EM algorithm, missing data ESL Ch. 8.5; PRML Ch. 9.3-9.4
Week 11
3/28/2017
[PDF] mixtures of Gaussians PRML Ch. 9.2; CML Ch. 14 Homework 4 out (see Courseworks)

3/30/2017
[PDF] matrix factorization Review article Due April 12 by 11:59pm
Week 12
4/4/2017
[PDF] non-negative matrix factorization ESL Ch. 14.6; Review article

4/6/2017
[PDF] latent factor models, PCA and variations ESL Ch. 14.5; PRML Ch. 12.1-12.3
Week 13
4/11/2017
[PDF] Markov models PRML Ch. 13.1 Homework 5 out (see Courseworks)

4/13/2017
[PDF] hidden Markov models PRML Ch. 13.2 Due April 23 by 11:59pm
Week 14
4/18/2017
[PDF] continuous state-space models PRML Ch. 13.3

4/20/2017
[PDF] association analysis ESL Ch. 14.2; Book chapter
Week 15
4/25/2017
[PDF] model selection SL Ch. 7.5-7.7; PRML Ch. 4.4

4/27/2017

Final in-class exam (covers material starting from Week 10)