Catalogue · Faculty of Machine Learning
ML 301
Machine Learning (CS229)
Stanford’s CS229 — the graduate counterpart to ML 201, with the mathematics restored. Generalised linear models, kernel methods, the EM algorithm, generalisation theory, and an introduction to reinforcement learning. The recorded lectures and famously thorough lecture notes are open; the problem sets will test whether the Mathematics faculty did its job.
Syllabus
- generalised linear models
- SVMs and kernels
- EM algorithm
- learning theory
- reinforcement learning
Held at Stanford University. The materials remain theirs; the structure is ours. Finished it? Record it in your transcript.