EECS
- CS 523 (Advanced Algorithms)
Highly recommended for you to take if you are a PhD student.
- ECE 565 (Estimation, Filtering and Detection)
Covers similar material to ST 562 AND ST 623. If you haven't taken those two courses, then most of the material in ECE 565 will be new.
- ECE 566 (Information Theory)
No one in the lab meeting had taken this before.
Stat
- ST 561, 562, 563 (Theory of Statistics)
Must take for ML students
- ST 559 (Bayesian Statistics)
Only offered every 2nd year (alternates with ST 565). Highly recommended for ML students.
- ST 557 (Multivariate Statistics)
Covers clustering, dimensionality reduction, etc. Very useful for ML and vision research.
- ST 565 (Time Series and Spatial Statistics)
Alternates with ST 559. Useful if you work on time series or spatial data.
- ST 581 (Linear Programming)
Very good introductory course on optimization. If you don't want to take this course, Andrew Ng has a good introductory tutorial in CS 229
- ST ___ (Non-linear Programming)
Not sure what the number is
- ST 623 (Generalized Linear Models)
Moy says this is very useful.
ME/ENGR
- ME 538 (Multi-agent Systems)
No one in the lab meeting had taken this before.
- ENGR 521 (Applied Robotics)
Yonglei is taking it this quarter and will tell us about it at the end of the quarter
