CS 6375: Machine Learning

Fall 2015

Course Info

Where: ECSS 2.410
When: MW, 11:30am-12:45pm

Instructor: Nicholas Ruozzi
Office Hours: Tuesday 11am-12pm and by appointment in ECSS 3.409

TA: Baoye Xue
Office Hours: Monday and Wednesday 5pm-6pm in the Clark Center CN 1.202D

Grading: problem sets (50%), midterm (20%), final (30%)
Attendance is MANDATORY. The instructor reserves the right to lower final grades as a result of poor attendance.

Prerequisites: some familiarity with basic probability, algorithms, multivariable calculus, and linear algebra.

Schedule & Lecture Slides

Week Dates Topic Readings
1 Aug. 24 & 26 Introduction & Regression
Bishop, Ch. 1
2 Aug. 31 & Sept. 2 Support Vector Machines
Duality & Kernel Methods
Lecture Notes by Andrew Ng
Bishop, Ch. 7 for a different perspective
Barber, Ch. 17.5
Boyd, Ch. 5
3 Sept. 9 Support Vector Machines with Slack Bishop, Ch. 7.1, SVMs & SVMs with slack
4 Sept. 14 & 16 Decision Trees
k Neareset Neighbor
Mitchell, Ch. 3
Bishop, Ch. 14.4
5 Sept. 21 & 23 Learning Theory & PAC Bounds
VC Dimension & Bias/Variance Trade-off
6 Sept. 28 & 30 Bias/Variance Trade-off & Bagging
Bishop, Ch. 14
Hastie et al., Ch. 8.7 & Ch. 15
Short Intro. to AdaBoost
7 Oct. 5 & 7 Clustering
Hastie et al., Ch. 14.3.6, 14.3.8, 14.3.9, 14.3.12
Bishop, Ch. 9.1
PCA Notes
8 Oct. 12 & 14 Midterm (in class Oct. 12)
Bayesian Methods
Bishop, 2-2.3.4
9 Oct. 19 & 21 Naive Bayes
Logistic Regression
Bishop, 1.5, 4.2-4.3.4
10 Oct. 26 & 28 Gaussian Mixture Models
Hidden Markov Models
Bishop, 8.4.1, 13.1-2, 9.2, 9.3, 9.4
Other HMM Notes
Other Mixture Model Notes (1) (2)
11 Nov. 2 & 4 Bayesian Networks
LDA Survey Article
Intro. to Bayesian Networks
Bishop 8.1
12 Nov. 9 & 11 Neural Networks
Nielsen Ch. 1-3
Bishop 5.1-5.3
ConvNetJS Demos
13 Nov. 16 & 18 Collaborative Filtering
Active Learning
14 Nov. 30 & Dec. 2 Reinforcement Learning
Learning to Rank
15 Dec. 7 & 9 Course Summary Sample Exams [1] [2]
(some material was not covered this semester)

Problem Sets

All problem sets will be available on the eLearning site and are to be turned in there. See the homework guidelines below for the homework policies.

Textbooks & References

The following textbook will be used for the course. The following books may also serve as useful references for different parts of the course.


All exams will be closed book and notes.
Midterm: October 12, in class.
Final: December 16.

Homework Guidelines*

I expect you to try solving each problem set on your own. However, if you get stuck on a problem, I encourage you to collaborate with other students in the class, subject to the following rules:
  1. You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.
  2. Once you solve the homework, you must write up your solutions on your own, without looking at other people's write-ups or giving your write-up to others.
  3. In your solution for each problem, you must write down the names of any person with whom you discussed it. This will not affect your grade.
  4. Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!
Late homeworks will NOT be accepted except in extreme circumstances or those permitted by university policy (e.g., a religious holiday). All such exceptions MUST be cleared in advance of the due date.

*adpated from David Sontag