- [Apr. 2] Course webpage is setup ready.
- [Aug. 2] Course Piazza page is setup ready.
- [Aug. 2] Course collab page is setup ready.
- [Aug. 3] Course schedule page is setup ready.
- [Aug. 26] Q0 is out. Please evaluate yourself. This quiz is the minimum math requirements for the course.
- [Sept. 3] HW1 is out. due in two weeks.
- This is a graduate-level machine learning course.
- Machine Learning is concerned with computer programs that automatically improve their performance through experience. This 3-credit course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory.
- Assignments include a student project, multiple short programming and writing assignments for hands-on experiments of various learning algorithms, multiple in-class quizzes, and an in-class mid-term exam.
- Required courses as prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills and can program with python (required ! ).
- Course Collab page to submit assignments and project reports.
- Course schedule and materials are listed @
- Course Piazza page for QA of exams, quizzs, class-discussions, assignments and project reports.
- No required text book.
- Course slides and handouts are mostly self-contained.
Course Grading Policy
The grade will be calculated as follows:
Additional Non-Linear Constraint
- Assignments (50%, with each assignment 10%)
- Midterm (25%)
- Final project (25%)
Assignment due dates, Lateness
- In order to pass the course, the average of your midterm and final
must also be "pass".
- Unless otherwise specified, assignments should be submitted through collab and are due
at 11:59pm on the due date .
- Multiple in-class quizzes will be given over the whole semester. Please do not miss classes in order to take these pop quizzes.
- Each student has three extension days to be used at his or
her own discretion throughout the entire course. Your grades would be
discounted by 15% per day when you use these 3 late days.
You could use the 3 days in whatever combination you like. For example,
all 3 days on 1 assignment (for a maximum grade of 55%) or 1 each day
over 3 assignments (for a maximum grade of 85% on each).
After you've used all 3 days, you cannot get credit
for anything turned in late.
- 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 Sept. 5, 2015, please
send mail to the instructor.
- Errata and answers to questions are being discussed and answered
on the course piazza size and through emailist.
The Course Schedule Reference : The official Academic Calendar at