- [Jan. 2] Course webpage is setup ready.
- [Jan. 20] Reading assignments are due every Tue.
- [Mar. 20] Project Mid-phase Report is due.
- This is an advanced graduate-level machine learning course.
- The course takes the form of half-seminar and half-project to complement the lectures by the instructor.
- This course offers opportunities for students to learn the
state-of-the-art advanced machine learning / data mining methods
focusing on large-scale cases. Students are expected to generate
top-tier publications when finishing the course.
Instructor's Permission for enrollment is required for this
- Required courses as prerequisite: Graduate-level machine
learning; Graduate-level Architecture .
- Graduate-level Optimization and Graduate-level OS are preferred.
Course Grading Policy
The grade will be calculated as follows:
- 45% for the participation and in-class paper presentations/discussions (we have 30 classes till April 29th);
- 50% for the quality of the class project delivered by each team;
- 5% for a few in-class quizzes in this course.
- Sit-in: No. This course is for registered students only.
- On every Tuesday, the whole class will meet @ Rice Hall;
- Instructor will provide a lecture about the relevant topic for the first half of the class;
- The whole class will discuss the readings (assigned in the previous week) during the second half of the class;
- Each student is expected to prepare a slide with (at least) 3 pages
summarizing each assigned reading.
- Each week, we will assign 2 to 3 reading materials (video lectures or papers or research lecture slides )
- Template of the summary:
- 1. Motivations / Why needed ? / Why important ?
2. Previous solutions
3. Key insights
4. Key equations
5. Key conclusions
- Students are picked up randomly to present their slides of the reading
- On every Thursday, each team takes turns to meet the instructor for 30mins @ Rice Hall 503 (see agenda on collab);
- Each team is required to report their progress formally on the project (weekly summary + plan of action for next week);
- I expect each team will generate a formal publication to a relevant top-tier conference by the end of the semester.;
- Each project should be done in a team of 1 to 3 students.
- Each student's class project carries 50% weight in grading and is expected to span about 15 weeks.
General timeline about the project:
- Week3: Submission of project proposal ;
- Week10: Submission of project mid-phase report;
- Week15: Submission of presentation slides;
- Week16 (Tu - 0428): Project presentation. Each presentation will be 10-minutes long with 2 minutes for questions.
- Week16 (Sat) : Submission of final report. Each report is expected to be at least 9 page long. A latex template will be provided.
- Announcements are being emailed to the course mailing list.
- A welcome note will be sent to the mailing list early in the semester.
- If you do not receive the welcome message by Jan 15, 2014, please
send mail to the instructor.
- Errata and answers to questions are being discussed and answered
on the course emailist.
The Course Schedule Reference : The official Academic Calendar at