COMP 875 Machine Learning Methods in Image Analysis

What the class is about
“Applied” machine learning and statistical methods
Applications are primarily, though not exclusively, to computer vision and medical imaging
Students from other research areas are welcome
Exact list of topics to be determined by you!

Who should take this class?
This is meant as an “advanced” graduate course
Ideally, you should have taken COMP 665, 775, 776, or Data Mining (or similar courses elsewhere)
You should be comfortable reading and understanding papers in recent conferences such as CVPR, ICCV, MICCAI, NIPS, ICML, etc.
You should have some experience doing research presentations
If you have questions or doubts about your background, please talk to me after this class

Why Machine Learning?
Image analysis early on: simple tasks, few images
L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph.D. thesis, MIT Department of Electrical Engineering, 1963.

Why Machine Learning?
Image analysis early on: try to program a computer directly using rules and symbolic representations
Y. Ohta, T. Kanade, and T. Sakai, “An Analysis System for Scenes Containing objects with Substructures,” Proceedings of the Fourth International Joint Conference on Pattern Recognition, 1978, pp. 752-754.

Today: Lots of data, complex tasks
Why Machine Learning?

Today: Lots of data, complex tasks
Instead of trying to encode rules directly, learn them from examples of inputs and desired outputs
Why Machine Learning?

Not Just Image Analysis
Speech recognition
Document analysis
Spam filtering
Computer security
Statistical debugging
Bioinformatics
….

Topics (tentative)
Classifiers: linear models, boosting, support vector machines
Kernel methods
Bayesian methods, Expectation Maximization
Random field models
Sampling techniques such as Markov Chain Monte Carlo
Unsupervised learning: density estimation, clustering
Manifold learning and dimensionality reduction
Distance metric learning
Semi-supervised learning
Online and active learning
Sequential inference (i.e., tracking)
Large-scale learning

Class requirements
Class format: lectures and student presentations
Grading:
Presentation: 35%
Project: 35%
Participation: 30%

Presentation
You are “professor for a day”: you need to give a one-hour lecture that would be interesting and accessible to all the students in the class
You are responsible for selecting your own topic and paper(s)
Look at the list of reading materials on the class webpage
Look through recent conference proceedings
Pick a topic of interest based on your own research

Presentation Guidelines
Evaluation criteria
Integration: utilize multiple sources
Critical thinking: separate the essential from the non-essential; critique the papers you present; think of alternative applications and future research directions
Interactivity: try to involve the rest of the class
Structuring the presentation
Will depend on your focus
Broadly speaking, you may want to focus either on a particular learning topic, or a particular application

Sample Presentation Outline
Introduction
Problem definition
Problem formulation
Significance
Survey of methods for solving this problem
Detailed presentation of one or more specific methods
Discussion
Pluses and minuses of different methods
Compare and contrast different approaches
Ideas for improvement and future research
Alternative applications
Alternative methods for solving the same problem
Connect your topic to other topics discussed earlier in class

Presentation Timeline
Reading list: due next Thursday, September 3rd
Preliminary slides: due Monday the week before your scheduled presentation
Practice meeting: scheduled for the week before your presentation
Final slides: due by the end of the day after your presentation
All of the above are part of your presentation grade (35% of total class grade)
A note on slides: you must explicitly credit all sources

Project
Your project topic may be the same as your presentation topic
Not required, but may make your life easier
Two options: implementation or survey paper

Implementation
Implement one or more methods from literature
Conduct a comparative evaluation
Implement your own ideas or extensions of existing methods
Deliverable: an “informal” final report and (possibly) a short presentation
Students may collaborate, but each must submit his/her deliverables
You can use existing code and/or software, provided you document all your sources and it doesn’t make your project trivial

Survey Paper
Comprehensive tutorial, literature review
A “formal” academic paper
Typeset in LaTeX, 10-15 pages (single-spaced, 11pt font)
Must be individual

Project timeline (tentative)
Project proposal: due end of September (details to follow)
Progress report (for implementation) or draft paper (for survey, ~5 pages): due end of October
Final report or paper: due last day of class (December 8th)
All of the above are part of your project grade
(35% of total class grade)

Participation (30% of the grade)
Class attendance, being on time
Answer questions in review sessions at the beginning of class
Be prepared
Read all the material before the class and come up with
~3 questions for discussion
I may call on anyone at any time
Participate in discussions
Send email to me and/or the class mailing group links to material that may be of interest
If you never speak up in class, the best grade you can get is P+!