Lecture Time:
TuTh 15:00pm-16:50pm Center Hall Room 115
See also zoom link on Canvas
Study Sections: (cancelled)
A01 F 9:00a-9:50am Warren Lecture Hall Room 2113
A02 F 10:00a-10:50am Warren Lecture Hall Room 2113
A03 F 11:00a-11:50am Warren Lecture Hall Room 2113
A04 F 12:00p-12:50pm Warren Lecture Hall Room 2113
TA:
Zeyuan Chen
Haiyang Xu
Text Books:
1. Christopher M. Bishop, "Pattern Recognition and Machine Learning", 2006.
2. R. Duda, P. Hart, D. Stork, "Pattern Classification", second edition, 2000. here
This course is self-contained; having the textbook is helpful but not absolutely necessary.
Office Hours (Calendar link available on Canvas and Piazza):
Zhuowen Tu, CSB 107, 17:00 pm -- 18:00 pm (Tuesday and Thursday)
Haiyang Xu, CSB 107, 16:30 pm -- 18:00 pm (Wednesday)
Zeyuan Chen, CSB 107, 16:30 pm -- 18:00 pm (Monday)
Course Description:
Supervised Machine Learning Algorithms: this course will prepare the students in basics of the statistical classification methods which will likely serve the foundation for data analysis and inference in a variety of applications. It will also be helpful in learning more advanced statistical machine learning algorithms, which have been applied in a wide range of scenarios for studying and predicting cognitive models, financial models, social behaviors, brain growth patterns, and visual inference.
You will need to use Python to do your assignments and final project.
Grading policy:
Homework Assignments (6 assignments, dropping the lowest scoring one, ; note that the total points on the HWs will be capped and the bonus credit will only be used to help with the lost points in the assignments): 40%
Midterms: 40%
Final project: 20%
Bonus points: 3% (Piazza activities + final project)
Late policy: 5% reduction for the first day and 10% reduction afterwards for every extra day past due for the homework assignments and the final project.