COMP 875: Machine Learning Techniques in Image Analysis
Fall 2009, Tuesdays and Thursdays 3:30-4:45, FB 009
Instructor: Svetlana Lazebnik (lazebnik -at- cs.unc.edu)
Quick links: schedule, reading materials,
About the Course
This is a graduate seminar course on statistical and machine learning techniques.
It is intended as a standard part of the medical imaging
and computer vision graduate curriculum, to be taken by students who have already taken COMP 665, 775, or 776.
However, students from other research areas who want to get a better background in machine learning
are also encouraged to attend. For example, students who have taken a Data Mining course would be
well prepared for this one.
The course will consist of a series of introductory lectures, followed by student presentations.
The selection of presentation topics will be driven by the
interests of students who are enrolled. Possible topics include, but are not limited to, the following:
For more detail, see this list of reading materials.
- Classifiers: linear models, support vector machines, boosting
- Kernel methods
- Bayesian methods, EM
- 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
Each student will be required to make
one or two presentations (depending on enrollment) on his or her chosen topic
(see this list for possible topic ideas).
The presentations should be created with the following criteria in mind:
is an example of a good presentation from a similar course at UT Austin.
To help everybody prepare a high-quality presentation, there will be a couple of
intermediate steps. In the first few weeks of the course, you will be
required to submit a reading list for your presentation (this may be combined
with your project proposal, see below). A week before you are scheduled to present,
you will be required to send me a preliminary version of your slides and
meet with me to discuss it.
It is highly recommended that you build your project around the same topic as your presentation
(this will save you a lot of effort in the course). You have two options:
- Integration: think of yourself as professor for a day. You should strive to give a comprehensive and
understandable lecture introducing the class to a specific topic in machine
learning. Even if your presentation centers on one particular method or application,
you need to consult multiple sources and give a survey of relevant literature.
- Critical thinking: you are expected to give a balanced and critical
perspective on the topic you are presenting. You should go beyond simply giving a list
of relevant papers or describing the techniques. Compare and contrast
different papers, question assumptions, expose possible flaws in experimental protocol,
identify limitations, suggest alternative applications and/or directions for future research.
- Interactivity: try to involve the class. Ask for input from the class,
try to think of discussion topics. This part should follow naturally if you get in
the "critical thinking" mindset.
Deliverables throughout the semester will consist of a proposal (which may be integrated
with the reading list for your presentation),
one progress report (in the case of a survey paper, this will be a first draft),
and a final report.
- Implementation: do an empirical evaluation of one or more methods,
apply an existing method to an interesting problem, extend an existing method,
or design and implement your own solution to a learning problem.
- Survey paper: you can think of this as an extended set of notes produced
based on your presentation slides. It should serve as a thorough introduction for anyone
who wishes to learn about your topic. The survey should be structured as a formal academic
paper. It should be 10-15 pages in length (single-spaced, single colum, 11pt) and typeset
- Participation and attendance: 30%
- Presentation (including reading list, preliminary slides and meeting, and final slides): 35%
- Project (including proposal, progress report, and final report): 35%
Some useful links