Lecture Notes: Optimization for Machine Learning

Abstract

Lecture notes on optimization for machine learning, derived from a course at Princeton University and tutorials given in MLSS, Buenos Aires, as well as Simons Foundation, Berkeley.

References in corpus (2)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Cited by in corpus (1)

• Accelerated gradient sliding and variance reduction