cLecture Time:
Tuesday and Thursday, 2pm-3:20pm, Zoom
Study Sections:
Kwonjoon Lee: Tuesday (8:00pm-9:00pm), Zoom
TA:
Kwonjoon Lee kwl042@eng.ucsd.edu
IA:
Yu-Chieh Chen yuc399@ucsd.edu
Daniel Lee
Jia Shi jis283@ucsd.edu
Yushan Wang yuw688@ucsd.edu
Text Books:
This course is self-contained and we will make the course slides available online, as well as various useful links.
Office Hours:
Zhuowen Tu: Tuesday (3:30pm-4:00pm, 7:30pm-8:00pm), Thursday (3:30pm-4:30pm), Zoom
Kwonjoon Lee: Friday (4:00pm-5:00pm), Zoom
Yu-Chieh Chen: Sunday (7:00pm-8:00pm), Zoom
Daniel Lee: Saturday (12:00pm-1:00pm), Zoom
Jia (Elvis) Shi: Monday (6:00-7:00pm), Zoom
Yushan Wang : Wednesday 2:00- 3:00 pm. Zoom:
Office Hours in the finals week (Dec. 14 - Dec. 18, 2020)
Zhuowen Tu: Monday (Dec. 14), Tuesday (Dec. 15), Wednesday (Dec. 16), 10:00am-10:30 am, 19:00pm-19:30pm
Kwonjoon Lee: Tuesday (Dec. 15) 4pm-5pm, Thursday (Dec. 17) 2pm-3pm
Yu-Chieh Chen: Monday (Dec. 13), 6:00pm-7:00pm
Jia (Elvis) Shi: Dec. 13. Monday (7:00-8:00pm)
Daniel Lee: Wednesday (Dec 16), 1:00pm-2:00pm
Yushan Wang: Wednesday(Dec 16) 2:00pm -3:00pm
Websites:
Course website (Syllabus, Slide links, Homework links, ...)
Piazza (For posting homework assignments, lecture slides, questions, answers, announcements, polls, ..)
Gradesope (For submitting your homework assignments)
Canvas (Lecture videos, midterm, )
Course Description:
This course is an advanced seminar and project course that follows the Natural Computation courses. Advanced and new machine learning methods will be discussed and studied. We will go through some popular topics in machine learning covering:
(1) Multi-class and multi-label classification, (2)Structural prediction (Structural SVM, Conditional random fields), (3) Hidden Markov models, (4) Recurrent neural networks, (5) Semi-supervised learning and weakly-supervised learning, (6) Compressed sensing, sparsity and low-rank, (7) Meta-learning and Reinforcement learning
Prerequisites:
COGS 118A, or CSE151A, or CGOS181, or consent from the instructor.
Grading policy: (tentative)
Assignments: 50%
Midterm: 20%
Final project: 30%
Bonus point: 3% (Piazza, final project)