Spring semester is over, yay! To celebrate summer, I’ve compiled lecture notes from the graduate course COS 598D, a.k.a. “optimization for machine learning“.
The course is an aftermath of a few lectures and summer school tutorials given in various locations, in which lectures goal of the course was to present the most useful methods and ideas in a rigorous-but-not-tedious way:
- Suitable for a mathematically-prepared undergraduate student, and/or researcher beginning their endeavor into machine learning.
- Focus on the important: we start with stochastic gradient descent for training deep neural networks.
- Bring out the main ideas: i.e. projection-free methods, second-order optimization, the three main acceleration techniques, qualitatively discuss the advantages and applicability of each.
- *short* proofs, that everyone can follow and extend in their future research. I prefer being off by a constant/log factor from the best known bound with a more insightful proof.
- Not loose track of the goal: generalization/regret, rather than final accuracy. This means we talked about online learning, generalization and precision issues in ML.
The most recent version can be downloaded here:
This is still work in progress, please feel free to send me typos/corrections, as well as other topics you’d like to see (on my todos already: lower bounds, quasi-convexity, and the homotopy method).
Note: zero-order/bandit optimization is an obvious topic that’s not address. The reason is purely subjective – it appears as a chapter in this textbook (that also started as lecture notes!).