Lecture Time:
Tuesday and Thursday 11:00am-12:20pm CENTR 115
Study Sections:
A01 Friday 9:00a-9:50a CENTR 222
A02 Friday 2:00p-2:50p CSB 005
TA:
Xiang Zhang
Divyansh Srivastava
IA:
Melody Yin
Zehan Li
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:
Zhuowen Tu: 12:30pm-13:30pm, CSB 107 (Tuesday, Thursday), 19:00-20:00pm, Zoom (lecture link) (Tuesday)
Melody Yin: Monday 9am-10am, Zoom: https://ucsd.zoom.us/j/92564702621
Xiang Zhang: Monday 2pm-3pm, Zoom: https://ucsd.zoom.us/j/93694332208
Divyansh Srivastava: Wednesday 1pm-2pm, Zoom: https://ucsd.zoom.us/j/98856866105
Zehan Li: Wednesday 5pm-6pm, Zoom: https://ucsd.zoom.us/j/92439727439
Office hours for the finals week:
Zhuowen Tu: Monday, 12/09, 10:00 AM–11:00 AM, Tuesday, 12/10, 7:00 PM–8:00 PM, Wednesday, 12/11, 7:00 PM–8:00 PM, Thursday, 12/11, 10:00 AM–11:00 AM, via lecture zoom links.
Piazza
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.