Jason J. Corso
Teaching List
Contents Links
Michigan Courses (2014-)
Tutorials
Buffalo Courses (2007-2014)

Michigan Courses
2016-2017
Winter EECS 542 Advanced Topics in Computer Vision
Fall EECS 504 Foundations of Computer Vision
2015-2016
Winter EECS 542 Advanced Topics in Computer Vision
Fall EECS 598-01 Foundations of Computer Vision
2014-2015
Winter EECS 598-04 Probabilistic Graphical Models
Fall EECS 598-08 Foundations of Computer Vision

Tutorials
2013-2014
CVPR 2014 Tutorial on Video Segmentation

Buffalo Courses
2013-2014
Spring CSE 704: Seminar (Joint Visual, Lingual and Physical Models and Inference Algorithms)
2012-2013
Fall CSE 672: Bayesian Vision
Spring CSE 455/555: Introduction to Pattern Recognition
2011-2012
Fall CSE 734: Seminar (Readings in Computer Vision and Machine Learning)
Spring CSE 455/555: Introduction to Pattern Recognition
2010-2011
Fall CSE 672: Bayesian Vision
  CSE 705: Vision Seminar (Spatiotemporal Video Analysis)
  UE 141 DD: Discovery Seminar in Computer Vision
Spring CSE 455/555: Introduction to Pattern Recognition
2009-2010
Fall CSE 642: Techniques in AI: Vision for HCI
  CSE 702: Seminar in Image Semantics
Spring CSE 555: Introduction to Pattern Recognition
2008-2009
Fall CSE 702: Seminar in Pattern Theory
Spring CSE 555: Introduction to Pattern Recognition
2007-2008
Fall CSE 702: Seminar in Medical Image Segmentation
Spring CSE 672: Vision as Bayesian Inference
2006
Spring BIOMED 223C: Object-Oriented Methods in Software Engineering for Medical Informatics (at UCLA)
This page gives a list and brief description of the courses I now teach (and have taught in past semesters).
  Winter 2017
EECS 542: Advanced Topics In Computer Vision
URL: http://web.eecs.umich.edu/~jjcorso/t/542W17/
Description: The course will focus on learning structured representations and embeddings for high-level problems in computer vision. Approaches for structured prediction, deep learning, and dictionary learning will be covered, all with an emphasis on modeling certain classes of structure, such as affine invariance and sparsity. The course will be highly interactive with a mix of readings, homeworks, quizzes and a course project.
  Fall 2016
EECS 504: Foundations of Computer Vision
URL: http://web.eecs.umich.edu/~jjcorso/t/504F16/
Description: Computer Vision seeks to extract useful information from images. This course covers the foundations of computer vision. It emphasizes computer vision as a search for visual invariants and computer vision as mathematical modeling. Foundational representations of images and image content will be discussed. Cross-cutting problems of reduction (e.g., feature extraction, segmentation), estimation (e.g., image alignment, camera calibration), and matching will be concretely defined and elaborated through many real examples from modern computer vision.
  Winter 2016
EECS 542: Advanced Topics In Computer Vision
URL: http://web.eecs.umich.edu/~jjcorso/t/542W16/
Description: The course will focus around learning structured representations and embeddings for numerous problems in computer vision. Approaches for learning from unimodal and multimodal data will be covered and include topics from sparse coding, convolutional neural networks and others. Multimodal problems will include topics at the boundary of vision and language, vision and speech, and vision and robotics. The course will be a mix of lecture, student presentation, and discussion; students will complete some programming assignments, paper reviews and presentations, and a project. Prior experience from EECS 442 or EECS 5XX (Foundations of Computer Vision) expected.
  Fall 2015
EECS 598-01: Foundations of Computer Vision
URL: http://web.eecs.umich.edu/~jjcorso/t/598F15/
Description: Computer Vision seeks to extract useful information from images. This course covers the foundations of computer vision. It emphasizes computer vision as a search for visual invariants and computer vision as mathematical modeling. Foundational representations of images and image content will be discussed. Cross-cutting problems of reduction (e.g., feature extraction, segmentation), estimation (e.g., image alignment, camera calibration), and matching will be concretely defined and elaborated through many real examples from modern computer vision.
  Winter 2015
EECS 598-04: Probabilistic Graphical Models in Vision and Beyond
URL: http://web.eecs.umich.edu/~jjcorso/t/598W15/
Description: Probabilistic graphical models have emerged as a powerful formalism for leveraging principled probability theory and discrete structured data representations to model large-scale problems involving hundreds or even thousands of often high-dimensional variables. Computer vision provides a set of such large-scale, concrete problems well-suited for probabilistic graphical models. The course will cover probabilistic graphical models in detail starting from the basics and pushing through contemporary results. There will be an emphasis on driving problem formulations from computer vision but our coverage will be broad; connections to other application areas will be discussed when plausible.
  Fall 2014
EECS 598-08: Foundations of Computer Vision
URL: http://web.eecs.umich.edu/~jjcorso/t/598F14/
Description: Computer Vision seeks to extract useful information from images. This course begins the fundamentals of image formation and then organizes the remaining material according to the class of information to be extracted. We will cover early processes, such as basic features, edges and contours; motion tracking, including optical flow and filtering; shape primarily from a binocular 3D reconstruction point of view; and both object and action detection and recognition. The course has been designed to present an introduction to computer vision targeted to graduate students. The course will balance theory and application both in lectures and assignments.
  CVPR 2014 Tutorial
CVPR 2014: Tutorial on Video Segmentation
URL: http://web.eecs.umich.edu/~jjcorso/t/CVPR14_videoseg/
Description: In recent years, segmentation has emerged as a plausible first step in early video processing of unconstrained videos, without needing to make an assumption of a static background as earlier methods have. Video segmentation and over-segmentation, or more commonly supervoxel extraction, is a complementary early video processing step to the more traditional feature extraction, such as STIP and trajectories, and it extends the long history of image segmentation methods. This tutorial will survey and present the important models and algorithms for video segmentation. We will cover direct extensions of image segmentation methods through video-specific spatiotemporal and streaming methods. In addition to core methodological elements, the tutorial will also cover benchmark and evaluation of video segmentation as well as applications of video segmentation. Participants will be introduced to the details of these methods not only through traditional slide presentations but also example implementations through the LIBSVX library.
  Spring 2014
CSE 704: Readings in Joint Visual, Lingual and Physical Models and Inference Algorithms
URL: http://www.cse.buffalo.edu/~jcorso/t/2014S_SEM/
Description: This seminar will study modeling and inference in the case that multimodal data is available. The data situations of focus on vision and language, but others will be considered, such as action (physical motion), audition, etc. The seminar will focus on reading and discussing topic-relevant research papers.
  Spring 2013
CSE 455/555: Introduction to Pattern Recognition
URL: http://www.cse.buffalo.edu/~jcorso/t/2013S_555/
Description: Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
  Fall 2012
CSE 672: Bayesian Vision
URL: http://www.cse.buffalo.edu/~jcorso/t/2012F_672/
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.
  Spring 2012
CSE 455/555: Introduction to Pattern Recognition
URL: http://www.cse.buffalo.edu/~jcorso/t/2012S_555/
Description: Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
  Fall 2011
CSE 734: Seminar: Readings in Computer Vision and Machine Learning
URL: http://www.cse.buffalo.edu/~jcorso/t/2011F_SEM/
Description: This is a seminar course in advanced topics in computer vision and machine learning. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.
  Spring 2011
CSE 455/555: Introduction to Pattern Recognition
URL: http://www.cse.buffalo.edu/~jcorso/t/2011S_555/
Description: Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
  Fall 2010
CSE 672: Bayesian Vision
URL: http://www.cse.buffalo.edu/~jcorso/t/2010F_672/
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.
CSE 705: Vision Seminar (Spatiotemporal Video Analysis)
URL: http://www.cse.buffalo.edu/~jcorso/t/2010F_SEM/
Description: This is a seminar course covering topics in spatiotemporal video analysis. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.
UE 141 DD: Discovery Seminar in Computer Vision
URL: http://www.cse.buffalo.edu/~jcorso/t/2010F_DIS/
Description: This seminar will explore the intriguing and often misunderstood field of Computer Vision---automatic computer interpretation of visual data. We will read about and watch various portrayals of core Computer Vision problems in popular culture, such as Robocop and Minority Report. In class, we will discuss these core research problems in light of the readings and videos to allow the students to build a understanding of where the exciting field of Computer Vision is now and where it is going in the future. We will rely heavily on the Computer Vision Fact and Fiction project at UCSD.
  Spring 2010
CSE 555: Introduction to Pattern Recognition
URL: http://www.cse.buffalo.edu/~jcorso/t/2010S_555/
Description: Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
  Fall 2009
CSE 642: Techniques in AI: Vision for HCI
URL: http://www.cse.buffalo.edu/~jcorso/t/2009F_642/
Description: The promise of computer vision for enabling natural human-machine interfaces is great: vision-based interfaces would allow unencumbered, large-scale spatial motion; they could make use of hand gestures, movements, or other similar natural input means; and video itself is passive, cheap, and nearly ubiquitous. In the simplest case, tracked hand motion and gesture recognition could replace the mouse in traditional applications, but, computer vision offers the additional possibility of using both hands simultaneously, using the face, incorporating multiple users concurrently, etc. In this course, we will develop these ideas from both a theoretical and a practical perspective. From the theoretical side, we will cover ideas ranging from interaction paradigms suitable for vision-based interfaces to mathematical models for tracking (e.g., particle filtering), modeling high-dimensional articulated objects, and modeling a grammar of interaction, as well as algorithms for rapid and real-time inference suitable for interaction scenarios. From the practical side, we will each build (in pairs) an actual working vision-based interactive system. Each project must "close the loop" and be integrated directly into an interactive computer system (e.g., sort photos on the screen by grabbing them with each hand and moving them around). During the semester, very practical-minded topics such as interactive system design and architecture, debugging programs that process high-dimensional video data, and program optimization will be discussed alongside the underlying computer vision theory.
CSE 702: Seminar in Image Semantics
URL: http://www.cse.buffalo.edu/~jcorso/t/2009F_702/
Description: This course will explore the topic of semantics in image and video analysis. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.
  Spring 2009
CSE 555: Introduction to Pattern Recognition
URL: http://www.cse.buffalo.edu/~jcorso/t/2009S_555/
Description: Foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
  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://web.eecs.umich.edu/~jjcorso/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.

last updated: Mon Jan 16 14:28:41 2017; copyright jcorso
Please report broken links to Prof. Corso jjcorso@eecs.umich.edu .