CSE512: Machine Learning

Spring 2016, Time: Tues-Thurs 5:30-6:50PM, Location: Javits 111
Instructor: Minh Hoai Nguyen, New CS 153.
Office hours: Mons 5-6.30pm & Weds 4-5.30pm

TA: HeeYoung Kwon (heekwon@cs). Office hours: Fridays 12-2pm @ old cs 2203

Note for students who cannot register the course now

The class is currently full, but some people might drop within the first two weeks. If you show up in the first three lectures, we might be able to enroll you in, but no promise.


Machine Learning is centered around automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns to predict the future and make decisions. Examples include document retrieval, image classification, spam filtering, face detection, speech recognition, decision making, and robot navigation. This course covers practical algorithms and some theory of machine learning. Students will have hands-on experiments with various learning algorithms through a set of programming assignments. There will be a course project where students apply machine learning to an area of their interest.

Tentative Syllabus

Date Topic Readings Assignments
26-Jan-2016 Course introduction and Quiz 1 Andrew Moore's tutorial on probability Murphy's Chapter1
28-Jan-2016 MLE & MAP Mitchell's New Chapter on MLE and MAP
02-Feb-2016 Linear Regression Bishop's Section 3.1 and 3.3
04-Feb-2016 Bias-Variance Tradeoff Bishop's Sect. 3.2 HW1 is out
09-Feb-2016 Regularization, Ridge Regression, LASSO. Bishop's Section 3.1 and 3.3
11-Feb-2016 Naive Bayes
16-Feb-2016 Logistic Regression
18-Feb-2016 Generative versus Discriminative Classifier HW1 due. HW2 out.
23-Feb-2016 SVM, max-margin concept and primal formulation
25-Feb-2016 SVM, surrogate losses and Stochastic Gradient Descent
01-Mar-2016 Dual SVM and Kernel trick
03-Mar-2016 Hard example mining and Boosting HW2 due. HW3 out.
08-Mar-2016 Boosting Project proposal due.
10-Mar-2016 K-means and PCA
15-Mar-2016 Spring break - no class
17-Mar-2016 Spring break - no class HW3 due. HW4 out.
24-Mar-2016 Mid-term Exam
05-Apr-2016 Project Mid-report due
07-Apr-2016 HW4 due. HW5 out.
21-Apr-2016 HW5 due
28-Apr-2016 Final class
03-May-2016 Poster 1 - no class Project Poster 1: 3-7pm
05-May-2016 Poster 2 - no class Project Poster 2: 3-7pm
12-May-2016 No class Project report due
17-May-2016 Final exam - no class Final Exam 11:15AM ‐ 1:45PM

Intended Audience:

This course is intended for graduate students with interests in advancing or applying Machine Learning. Prerequisites include a foundation in Linear Algebra, Probabilities and Statistics, and the ability to program, especially Matlab.


There will be 5 homeworks, 1 mid-term exam, 1 final exam, 2 popup quizzes, and a final project. For the project, you will need to submit a proposal, a mid-report, and a final report. You will also need to present a poster during a poster session.

  • Homeworks: 40%

  • Midterm: 10%

  • Final exam: 20%

  • Project 25%

  • Quizzes 5%

Weights are approximate and subject to change. You are expected to do homeworks by yourselves. Even if you discuss them with your classmates, you should turn in your own code and write-up. Final projects can be done by one or two people. Two people projects will be scaled accordingly.

Recommended textbooks

Academic misconduct policy:

Don't cheat. Cheating on anything will be dealt with as academic misconduct and handled accordingly. I won't spend a lot of time trying to decide if you actually cheated. If I think cheating might have occurred, then evidence will be forwarded to the University's Academic Misconduct Committee and they will decide. If cheating has occurred, an F grade will be awarded. Discussion of assignments and projects is acceptable, but you must do your own work. Near duplicate assignments will be considered cheating unless the assignment was restrictive enough to justify such similarities in independent work. Just think of it that way: Cheating impedes learning and having fun. The assignments are meant to give you an opportunity to really understand the class material. Please also note that opportunity makes thieves: It is your responsibility to protect your work and to ensure that it is not turned in by anyone else. No excuses!

Disability note:

If you have a physical, psychological, medical or learning disability that may impact on your ability to carry out assigned course work, I would urge that you contact the staff in the Disabled Student Services office (DSS), Room 133 Humanities, 632-6748/TDD. DSS will review your concerns and determine, with you, what accommodations are necessary and appropriate. All information and documentation of disability is confidential.