Anil Aswani
4119 Etcheverry
Office hours – Tu 10-11A; Th 1-2P
aaswani [at] berkeley [dot] edu

Kevin Li
4176-A Etcheverry
Office hours – W 11-12P
kbl4ew [at] berkeley [dot] edu

TuTh 330-5P, 3109 Etcheverry

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 1, 2016. The project report (10-12 pages) is due on the last day of lecture April 28, 2016. Project proposals and reports should be submitted as a PDF file emailed to the GSI and cc'ed to the Instructor. 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 19

Convex Geometry; Course Syllabus

Jan 21

Complexity Measures

Jan 26

Sparse Linear Regression

Jan 28

Cross-Validation

Feb 02

M-Estimators

Feb 04

Local Linear Regression

Feb 11

Semiparametric Models

Feb 16

Matrix Completion

Feb 18

Paper: Low-Rank Approximation and Completion of Positive Tensors

Feb 25

Stability

Mar 01

Observability

Mar 03

Parametric Optimization

Mar 08

Reachability

Mar 10

Linear MPC

Mar 15

Tube MPC

Mar 17

Learning-Based MPC

Mar 29

The Oracle

Mar 31

Epi-Convergence

Apr 05

Papers: Semiparametric Regression for HVAC; Hybrid System Models of HVAC

Apr 07

Papers: LBMPC Control of HVAC; Comparing HVAC Controllers

Apr 12

Paper: Extensions of LBMPC for Quadrotors

Apr 14

Paper: Experiments of LBMPC on Quadrotor

Apr 19

Inverse Decision-Making

Apr 21

Estimating Multiple Utilities

Apr 26

Paper: Inverse Optimization with Noisy Data

Feb 16

Homework 1 – Due Thursday, March 3, 2016
winequality-red.csv

Mar 29

Homework 2 – Due Thursday, April 28, 2016