Lecture Time:
Tuesday and Thursday, 2pm-3:20pm, FAH 1450
Hybrid: In-person and online via Zoom.
Study Sections:
A01 W 3:00p-3:50p DIB 122
A02 W 4:00p-4:50p DIB 122
TA:
Zeyuan Chen
Haiyang Xu (hax038@ucsd.edu)
IA:
Sripad Karne
Athulith Paraselli
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 (CSB 107 1:00pm-1:30pm, 3:20pm-3:50pm), Tuesday (7:30pm-8:00pm, zoom only), Thursday (CSB 107 1:00pm-1:30pm, 3:20pm-3:50pm)
Haiyang Xu: Monday (CSB 107 2:30pm-3:30pm)
Zeyuan Chen: Wednesday (CSB 107 4:00pm-5:00pm)
Sripad Karne: Friday(11am-12pm, zoom only(https://ucsd.zoom.us/j/9632142221))
Office hours in the finals week
Zhuowen Tu (11:00am-12:00pm, Monday (06/09/25), Tuesday (06/10/25), and Thursday (06/12/25)), Class Zoom Link
Course Description:
This course is an advanced course that follows the basic Machine Learning methods, in particular along the line of supervised approaches. 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) Self-supervised learning, (8) Generative AI
Prerequisites:
COGS 118A, or CSE151A, or CGOS181, or consent from the instructor.
Grading policy:
Assignments (five): 50% (dropping the lowest HW; 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)
Midterm: 25%
Final project: 25%
Bonus point: 3% (Classroom participation, Piazza, final project)
Late penalty policy: a 5% deduction will be applied for the first day past due, followed by 10% everyday afterwards.