Prerequisites:

Mandatory:  Linear Algebra. Basic Probability Theory.

Recommended:  Signal Processing. Machine Learning.

Also Recommended:  18-799 by Joy Zhang would be an excellent course to take in parallel. This is also being conducted this fall.

LIST Of PROJECTS

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 and image processing problems.

The course will roughly follow the following outline.

There will be several guest lectures. These will be announced as dates are finalized.

Grading will be based on performance in course assignments and a final project.

Outline

Class 1, 25 Aug 2009	Introduction. Basics: Representing audio and image data.	Slides	Additional material
Class 2, 27 Aug 2009	Linear Algebra Refresher	Slides	Additional material
Class 3, 1 Sep 2009	Linear Algebra Refresher, Part II	Slides	Homework Problem
Class 4, 3 Sep 2009	DSP Refresher. Representing Sounds and Images.	Slides	Additional Material
Class 5, 8 Sep 2009	No class
Class 6, 10 Sep 2009	No class
Class 7, 15 Sep 2009	Eigen faces. Boosting. Face detection	Slides	Homework Problem	Homework Problem No 2.
Class 8, 17 Sep 2009	Component Analysis (Guest Lecture, De la Torre)	Slides
Class 9, 22 Sep 2009	Project Ideas (with Guests Speakers)	Slides	Eakta Jain's Slides	Avidan's Seamcarving video
Class 10, 24 Sep 2009	Speech synthesis, voice transformations (Guest Lecture, Black)	Slides
Class 11, 29 Sep 2009	Boosting, Face detection, Recaps.	Slides	Additional Material
Class 12, 1 Oct 2009	Independent Component Analysis (Guest Lecture, Smaragdis)	Slides Handout
Class 13, 6 Oct 2009	Latent variabe decomposition of audio signals	Slides	Additional Material
Class 14, 8 Oct 2009	Musical Onset Detection and Applications (Guest Lecture, Dannenberg)	Slides
Class 15, 13 Oct 2009	Overcomplete decompositions. Nearest-neighbor decomposition. Shift-invariant and transform invariant models.	Slides
Class 16, 15 Oct 2009	Non-negative matrix factorization and its application to audio (Guest Lec., Virtanen)	Slides
Class 17, 20 Oct 2009	Pitch estimation, voice distortion (Guest Lecture, Black)	Slides
Class 18, 22 Oct 2009	Shift-invariant decompositions; audio denoising.	Slides
Class 19, 27 Oct 2009	Music Identification (Guest Lecture, Sukthankar)	Slides
Class 20, 29 Oct 2009	Advanced component analysis (Guest Lecture, De la Torre)	Slides
Class 21, 3 Nov 2009	Iris recognition (Guest Lecture, Kumar)	Slides
Class 22, 5 Nov 2009	Automatic Speech Recognition in an Hour.	Slides
Class 23, 10 Nov 2009	Sparse and overcomplete representations	Slides
Class 24, 12 Nov 2009	Compressive Sensing (Boufonos)	Slides
Class 25, 17 Nov 2009	Microphone array processing	Slides
Class 26, 19 Nov 2009	Array processing -- maximum likelihood techniques, tracking, audio-visual tracing.	Slides
Class 27, 24 Nov 2009	Project presentations	Slides
Class 28, 1 Dec 2009	Project presentations	Slides
Class 29, 3 Dec 2009	Project presentations	Slides