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More advanced ML courses (only offered some years):
- CIS625: Computational Learning Theory
- theory side of ML (CS version)
- STAT928: Statistical Learning Theory
- theory side of ML (Stat version)
- CIS 700 Machine Learning and Economics
- ranking and choice models
- Lots of opportunities for independent studies in different research groups
- NLP, robotics, ML theory, medicine, ....
Statistics courses
- STAT542 - Bayesian Methods and Computation
- Shane Jenson on Bayesian methods (EM, Gibbs sampling)
- Multivariate methods
- Andreas Buja on multivariate methods (research oriented)
- STAT500 - Applied Regression and Analysis of Variance
- never underestimate the value of really understanding regression, but STAT 500 is too basic for people who have CIS520
- If you want the math side of regression, see STAT550 - Mathematical Statistics
- STAT553 - Machine Learning -- probably too basic after 520
- STAT701 - Modern Data Mining -- probably too basic after 520
Other related courses:
- CIS545 Big Data Analytics
- “data-parallel” approach to scaling computation
- ESE605: Modern Convex Optimization
- useful
Many courses go into detail on applications of machine learning to e.g. vision or NLP
- ESE 650 Learning in Robotics
- Dan Lee -- serious ML for Robotics
- CIS521: Intro to AI
- much easier than 520, with a bit of overlap (and not much ML)
- CIS580: Machine Perception
Again: there are also many options to do research in various labs around Penn.