Anil Aswani
4119 Etcheverry
Office hours – MW 10-11A
aaswani [at] berkeley [dot] edu


MWF 12-1P, in 3102 Etcheverry



Course on optimization; course on statistics or stochastic processes


(About 3-4) programming homeworks in R (50%); class project (50%)


This course will cover topics related to the interplay between optimization and statistical learning. The first part of the course will cover statistical modeling procedures that can be defined as the minimizer of a suitable optimization problem. The second part of the course will discuss the formulation and numerical implementation of learning-based model predictive control LBMPC, which is a new method for robust adaptive optimization that can use machine learning to provide the adaptation. The last part of the course will deal with inverse decision-making problems, which are problems where an agent's decisions are observed and used to infer properties about the agent.

Class Project:

The projects can be in the form of a literature review, a comprehensive application of data analysis methods, or involve the exploration of original research ideas. The project should be chosen in consultation with the course instructor, and a project proposal (two page summary) is due on March 5, 2014. The project report (10-12 pages) is due on the last day of classes May 2, 2014. Joint projects, involving reasonably sized groups, are allowed.


Specific topics that will be covered include:
  • Regression – Classical M-estimators; high-dimensional M-estimators; collinearity; semiparametric regression and Nadaraya-Watson regression
  • Learning-Based Model Predictive Control (LBMPC) – Robustness; consistent approximations; oracle design; software code generation
  • Inverse Decision-Making Problems – Inverse reinforcement learning; learning objective/utility functions; Learning utilities from game-theoretic equilibria described by variational inequalities

Lecture Notes:


Mar 3
Homework 1 – Due Monday, March 31, 2014
Apr 14
Homework 2 – Due Monday, April 21, 2014
Apr 21
Homework 3 – Due Monday, April 28, 2014