Jason J. Corso
Teaching List
This page gives a list and brief description of the courses I teach
(and have taught in past semesters).
| Fall 2008 | |
| CSE 702: | Seminar in Pattern Theory |
| URL: | http://www.cse.buffalo.edu/~jcorso/t/2008F_702/ |
| Description: | This seminar will focus on Grenander's Pattern Theory from a practical, contemporary perspective. Pattern Theory is the study of patterns from a representational perspective rather than a recognition one. Miller and Grenander write "Pattern theory attempts to provide an algebraic framework for describing patterns as structures regulated by rules, essentially a finite number of both local and global combinatory operations. Pattern theory takes a compositional view of the world, building more and more complex structures starting from simple ones. The basic rules for combining and building complex patterns from simpler ones are encoded via graphs and rules on transformations of these graphs." We will explore various theoretical aspects of modern pattern theory (e.g., probabilistic graphical models, grammars, matrix groups, information measures, manifolds, Markov processing and sampling) in the context of practical problems in computer vision and medical imaging. Students will be required to give one or two (depending on seminar size) prepared lectures during the semesters. Grading is S/U; letter grading is available as an option and requires a project. |
| Spring 2008 | |
| CSE 672: | Vision as Bayesian Inference |
| URL: | http://www.cse.buffalo.edu/~jcorso/t/2008spring_vbi/ |
| Description: | The course takes an in-depth look at various Bayesian methods in computer and medical vision. Through the language of Bayesian inference, the course will present a coherent view of the approaches to various key problems such as detecting objects in images, segmenting object boundaries, and recognizing objects. The course is roughly partitioned into two halves: modeling and inference. In the first half, it will cover both classical models such as weak membrane models and Markov random fields as well as more recent models such as conditional random fields, latent Dirichlet allocation, and topic models. In the second half, it will focus on inference algorithms. Methods include PDE boundary evolution algorithms such as region competition, discrete optimization methods such as graph-cuts and graph-shifts, and stochastic optimization methods such as data-driven Markov chain Monte Carlo. An emphasis will be placed on both the theoretical aspects of this field as well as the practical application of the models and inference algorithms. |
| Fall 2007 | |
| CSE 702: | Seminar in Medical Image Segmentation |
| URL: | http://www.cse.buffalo.edu/~jcorso/t/2007fall_smis/ |
| Description: | The seminar will survey the literature in medical image segmentation. Topics include knowledge-based heuristics, voxel-based statistics, contour evolution, hierarchical modeling, and learning-based approaches. We will focus on constructing a complete taxonomy of approaches in this area. Students will be required to make one paper presentation and do a project to explore a method, which can be new research, in detail. Familiarity with vision and medical image computing is suggested but not required. |
While a postdoc at UCLA.
| Spring 2006 | |
| BIOMED223C | Programming Lab in Medical Informatics III |
| Topic: | Object-Oriented Methods in Software Engineering for Medical Informatics |
| URL: | http://www.cse.buffalo.edu/~jcorso/t/biomed223c-spring06/ |
| Description: | The course is designed to expose the students to both relevant topics in medical informatics and the process of developing these topics into large software systems. The topic of emphasis for this quarter will be pattern classification. As a group, we will design the software framework necessary for a comprehensive pattern classification system. Collectively, the students will implement the designed framework. Individually, each student will implement a particular pattern classification algorithm in this framework. This project will be developed through the duration of the quarter as new topics in both software engineering and pattern classification are learned; the result will be a practical and complete system that the students can take with them into their future research. |