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

MWF 12-1P, in 3102 Etcheverry

http://ieor.berkeley.edu/~aaswani/sp14_ieor290a/

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.

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

Jan 22

Ordinary Least Squares; Course Syllabus

Jan 24

Bias-Variance Tradeoff

Jan 27

Stochastic Convergence

Jan 29

Cross-Validation

Jan 31

Classical M-Estimators

Feb 3

Collinearity

Feb 5

Abstract Structure

Feb 7

Lasso Regression

Feb 10

Extensions of Lasso

Feb 12

Nadaraya-Watson

Feb 14

Semiparametric Models

Feb 19

Partially Linear Models

Feb 21

Other Models

Feb 24

Stability

Feb 26

Controllability

Feb 28

Observability

Mar 3

Parametric Optimization

Mar 5

Lyapunov Theory

Mar 7

Reachability

Mar 10

Linear MPC

Mar 12

Robust Reachability

Mar 14

Tube MPC

Mar 17

Robustness in Tube MPC

Mar 19

Learning-Based MPC

Mar 21

The Oracle

Mar 31

Selected Variational Analysis

Apr 2

Consistent Approximation

Apr 4

L2NW in LBMPC

Apr 7

Semiparametric Regression for HVAC

Apr 9

Hybird System Models of HVAC

Apr 11

LBMPC Control of HVAC; Comparing HVAC Controllers

Apr 14

Extensions of LBMPC for Quadrotors

Apr 16

Extensions of LBMPC for Quadrotors; Experiments of LBMPC on Quadrotor

Apr 18

Experiments of LBMPC on Quadrotor

Apr 21

Numerical Implementation of Linear LBMPC

Apr 23

Inverse Decision Making

Apr 25

Estimating an Individual Utility

Apr 28

Details for Single Utility Learning

Apr 30

Estimating Multiple Utilities

May 2

Details for Multi-Agent Utility Learning

Mar 3

Homework 1 – Due Monday, March 31, 2014
winequality-red.csv

Apr 14

Homework 2 – Due Monday, April 21, 2014

Apr 21

Homework 3 – Due Monday, April 28, 2014