Anil Aswani
4119 Etcheverry
Office hours – TuTh 9-10A
aaswani [at] berkeley [dot] edu

Siyuan Sun
sysun [at] berkeley [dot] edu

TuTh 2-330P, 56 Barrows

http://ieor.berkeley.edu/~ieor265/

Course on optimization; course on statistics or stochastic processes

4 homeworks (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 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.

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 (one page summary) is due on March 3, 2015. The project report (10-12 pages) is due on the last day of lecture May 7, 2015. 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

Jan 20

Ordinary Least Squares; Course Syllabus

Jan 22

Local Linear Regression

Jan 27

Cross Validation

Jan 29

M-Estimators

Feb 03

Abstract Structure

Feb 05

Lasso Regression

Feb 10

Semiparametric Models

Feb 12

Nadaraya-Watson

Feb 17

Other Models

Feb 19

Stability

Feb 24

Observability

Feb 26

Parametric Optimization

Mar 03

Reachability

Mar 05

Linear MPC

Mar 10

Tube MPC

Mar 12

Learning-Based MPC

Mar 17

The Oracle

Mar 19

Epi-Convergence

Mar 31

L2NW in LBMPC

Apr 02

Semiparametric Regression for HVAC; Hybrid System Models of HVAC

Apr 07

LBMPC Control of HVAC; Comparing HVAC Controllers

Apr 09

Extensions of LBMPC for Quadrotors

Mar 17

Homework 1 – Due Thursday, April 2, 2015
winequality-red.csv

Apr 07

Homework 2 – Due Tuesday, April 14, 2015

Apr 21

Homework 3 – Due Tuesday, April 28, 2015