|This course is an elective in LTI, MLD and ECE|
|Timings:||4.30-5.50pm, Tuesdays and Thursdays|
|Location:||Porter Hall 125C|
|Mandatory: Linear Algebra. Basic Probability Theory.|
|Recommended: Signal Processing. Machine Learning.|
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.
Students from the previous iteration of the course (fall2009) worked on a number of excellent projects, several of which were submitted to conferences.
The planned course schedule will appear here shortly.
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.
The projects page: We had a terrific crop of projects this year
|Class 1, 24 Aug 2010||Introduction. Representing Sounds and Images.||Slides, Handout|
|Class 2, 26 Aug 2010||Introduction to Linear Algebra||Slides, Handout|
|Class 3, 31 Aug 2010||Introduction to Linear Algebra II||Slides, Handout||Homework 1|
|Class 4, 2 Sep 2010||DSP refresher: data parameterization||Slides, Handout||Additional material|
|Class 5, 7 Sep 2010||Eigen decomposition. Eigen faces. Face detection I.||Slides, Handout|
|Class 6, 9 Sep 2010||Project Ideas.||Slides, Handout||Additional material|
|Class 7, 14 Sep 2010||Boosting, Face detection.||Slides, Handout||Additional material|
|Class 8, 16 Sep 2010||Hidden Markov Model Basics, guest lecture by Raffay Hamid||No slides (blackboard class!)|
|Class 9, 21 Sep 2010||Expectation Maximization||Slides, Handout|
|Class 10, 23 Sep 2010||Towards Energy-Aware Facilities Through Minimally Intrusive Approaches (guest lecture by Maio Berges)||Slides|
|Class 11, 28 Sep 2010||Intel Open House|
|Class 12, 30 Sep 2010||Component Analysis, Part 1 (guest lecture, Fernando de la Torre)||Slides|
|Class 13, 5 Oct 2010||Component Analysis, Part 2 (guest lecture, Fernando de la Torre)||Slides|
|Class 14, 7 Oct 2010||Expectation Maximization (2), Clustering||Slides, Handout||Additional material|
|Class 15, 12 Oct 2010||HMMs -- part 2||Slides, Handout||Additional material|
|Class 16, 14 Oct 2010||Weighted finite state transducers, John McDonough||Slides not available|
|Class 17, 19 Oct 2010||Iris recognition (Guest lecture, Marios Savvides)||Slides not available|
|Class 18, 21 Oct 2010||No class|
|Class 19, 26 Oct 2010||Guest lecture, Roger Dannenberg||Slides||Additional material|
|Class 20, 28 Oct 2010||Automatic speech recognition||Slides|
|Class 21, 2 Nov 2010||Latent variable models.||Slides|
|Class 22, 4 Nov 2010||Speech synthesis||Slides||Additional material|
|Class 23, 9 Nov 2010||No class||Additional material|
|Class 24, 11 Nov 2010||Sound modification||Slides|
|Class 25, 16 Nov 2010||Kalman filtering||Slides|
|Class 26, 18 Nov 2010||Compressive sensing. Guest lecture, Petros Boufounos||Slides|
|Class 27, 23 Nov 2010||Extended kalman filtering||Slides|
|Class 28, 30 Nov 2010||No class||Slides|
|Class 29, 2 Dec 2010||Project Presentations||Slides|