CS 544
Spring 2012 All Classes
All Classes
Credit: 4 hours.
Applications of continuous and discrete optimization to problems in computer vision and machine learning, with particular emphasis on large-scale algorithms and effective approximations: gradient-based learning; Newton's method and variants, applied to structure from motion problems; the augmented Lagrangian method and variants; interior-point methods; SMO and other specialized algorithms for support vector machines; flows and cuts as examples of primal-dual methods; dynamics programming, hidden Markov models, and parsing: 0-1 quadratic forms, max-cut, and Markov random-fields solutions.
Section Status updates every 10 minutes.
| CRN | Type | Section | Time | Day | Location | Instructor | Section Details | |
|---|---|---|---|---|---|---|---|---|
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57440
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Lecture-Discussion
|
CV
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9:30AM
-10:45AM
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TR
|
1111 Siebel Center for Comp Sci
|
Forsyth, D
|
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