Machine Learning for Signal Processing
11-755/18-797, Fall 2015

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 such as audio including speech, images and video, and machine learning methods for a variety of speech/image processing and computer vision problems.

Prerequisites

Mandatory: Linear Algebra,Basic Probability Theory.

Grading Policies

Assignment (50%); Project (50%) -- Proposal (10%) + Midway Report (10%) + Final Report/Poster (30%)

Class Time and Location

Tuesdays and Thursdays, 3:00-4:20pm at GHC 4307

TAs and Office Hours

• Zhiding Yu: Fridays 4:00PM-5:00PM (Hamerschlag Hall B200 M6, yzhiding@andrew.cmu.edu)

• Bing Liu: Fridays 4:00PM-5:00PM (SV Campus Building 19 Room 1030, liubing@cmu.edu)

Misc

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