CSE512: Machine Learning

Spring 2018, Time: Mondays & Wednesdays 4-5.20pm, HARRIMAN HLL 137
Instructor: Minh Hoai Nguyen, New CS 153.
TA: TBA. Office hours: TBA

This course is intended for graduate students who already have good programming skills and adequate background knowledge in mathematics, including probability, statistics, and linear algebra. This course is offered by the Department of Computer Science, and students from the department will have priority in registering for this course. If you are not a Computer Science student, but believe that you have right prerequisites, complete this form to enter the queue for enrollment. The admin staff and I will process all special requests at the beginning of the Spring semester. Do not complete the form if you can enroll on Solar.

If both the class and the waitlist on Solar are full, do not send me an email. If you are still interested in taking the course, just come to the first two weeks of classes. Some spaces will be freed as many students will drop the course once they understand what machine learning really is. There is no guarantee though.

This page is under construction. More content will be posted closer to the start of Spring 2018 semester.


There will be six homework assignments and two exams.

  • Six homework assignments: 60%

  • Midterm exam: 15%

  • Final exam: 25%

Weights are approximate and subject to change. You are expected to do homework assignments by yourselves. Even if you discuss them with your classmates, you should turn in your own code and write-up. You can have one sheet of paper with notes in the exams.

Tentative Syllabus

Date Topic Readings Assignments
22-Jan-2018 Course introduction Andrew Moore's tutorial on probability Murphy's Chapter1
24-Jan MLE & MAP Mitchell's New Chapter on MLE and MAP
29-Jan Linear Regression Bishop's Section 3.1 and 3.3
31-Jan Bias-Variance Tradeoff Bishop's Sect. 3.2 HW1 is out
05-Feb Regularization, Ridge Regression, LASSO. Bishop's Section 3.1 and 3.3
07-Feb Naive Bayes
12-Feb Logistic Regression
14-Feb Generative versus Discriminative Classifier HW1 due. HW2 out.
19-Feb SVM, max-margin concept and primal formulation
21-Feb SVM, surrogate losses and Stochastic Gradient Descent
26-Feb Duality and Dual SVM
28-Feb Kernel trick & Hard example mining HW2 due. HW3 out.
05-Mar Boosting
07-Mar Neural Networks
12-Mar Spring break - no class
14-Mar Spring break - no class
19-Mar Mid-term review HW3 due. HW4 out.
21-Mar Mid-term exam - no class Mid-term Exam
26-Mar TBA
28-Mar TBA
02-Apr Deep Learning
04-Apr CNN HW4 due. HW5 out.
09-Apr RNN
11-Apr K-means and PCA
16-Apr GMM, EM Algorithm
18-Apr HMM HW5 due. HW6 out.
23-Apr Reinforcement Learning
25-Apr Reinforcement Learning
30-Apr Reinforcement Learning
02-May Final class HW6 due.
08-May Final exam - no class Final Exam 8.30pm - 11pm