Machine Learning for Signal Processing
11-755/18-797, Fall 2016

Bhiksha Raj
Language Technologies Institute, Carnegie Mellon University

Welcome to The MLSP Homepage

Signal Processing is the science that deals with extraction of information from signals of various kinds. This has two distinct aspects -- characterization and categorization. Traditionally, signal characterization has been performed with mathematically-driven transforms, while categorization and classification are achieved using statistical tools

Machine learning aims to design algorithms that learn about the state of the world directly from data.

A increasingly popular trend has been to develop and apply machine learning techniques to both aspects of signal processing, often blurring the distinction between the two.

This course discusses the use of machine learning techniques to process signals. We cover a variety of topics, from data driven approaches for characterization of signals including speech, images and video and other similar time-series and structured data, and machine learning methods for a variety of signal processing tasks aplied to these data.

Prerequisites

Mandatory: Linear Algebra,Basic Probability Theory.

Grading Policies

Assignment (60%); Project (30%) -- Proposal (5%) + Midway Report (5%) + Final Report/Poster (20%); Attendance and participation (10%)

Class Time and Location

Tuesdays and Thursdays, 3:00-4:20pm at Baker Hall, A 53

TAs and Office Hours

• Anurag Kumar: Office hours TBD (alnu@cs.cmu.edu)

• Chiyu Dong: Office hours TBD (chiyud@andrew.cmu.edu)

Misc

• All Homeworks are supposed to be coded in Matlab Only.