ECE 598PM: COMPUTATIONAL INFERENCE AND LEARNING, FALL 2016

Computational inference and machine learning have seen a surge of interest in the last 15 years, motivated by applications as diverse as computer vision, speech recognition, analysis of networks and distributed systems, big-data analytics, large-scale computer simulations, and indexing and searching of very large databases. This new course will introduce the mathematical and computational methods that enable such applications. Topics include computational methods for statistical inference, information theory, sparsity analysis, approximate inference and search, and fast optimization.

The course will complement ECE561 (Detection and Estimation), ECE544NA (Pattern Recognition and Machine Learning) and ECE598MR (Machine Learning) which introduce core theory for statistical inference and machine learning respectively, but do not focus on computational methods. Teaching materials include notes from the instructor and articles from scientific journals.

** Prerequisites: ** ECE490 and ECE534.

Office Hours: Time 10-11.30am Wednesdays, Room 310 CSL

Chapter 1: Introduction

Chapter 9: Bayesian Estimation

Chapter 12: Maximum Likelihood

by P. Moulin and V. Veeravalli 2013.

Lecture 1 (Introduction)

Lectures 2 and 3 (Bayesian Inference, ML, MAP, and MMSE)

Lecture 4 (Empirical Risk Minimization)

Lectures 5 and 6 (Stochastic Optimization)

Lecture 7 (Stochastic Integration)

Lecture 8 (Bootstrap)

Lecture 9 (Particle Filter)

Lecture 10-12 (EM Algorithm)

Notes: (Variational Inference)

Notes: (L1 Minimization)

Project Topic List

Homework 1 (due: Thurs Sep 29th). Solution.

Homework 2 (due: Tues Nov 1st.). Data file.

Homework 3(due: Thurs Dec1st.). Solution.

Midterm Solution