Probabilistic Graphical Models
MATH 275
Fall 2016 not offered
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Graphical models are used to represent complex, uncertain relationships among several, possibly very many, variables. They are fundamental in many domains of application, including medical diagnosis and prognosis, vision and image processing, robotics, and computational biology. This course will familiarize students with the graph theory and probability theory needed to discuss graphical models. After that, students will investigate exact and approximate statistical inference for graphical models, learning/inference of parameters, and possibly learning of graph structure. |
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| Credit: 1 |
Gen Ed Area Dept:
NSM MATH |
| Course Format: Lecture / Discussion | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: MATH222 |
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Fulfills a Requirement for: (COMP) |
Major Readings:
Daphne Koller and Nir Friedman, PROBABILISTIC GRAPHICAL MODELS.
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Examinations and Assignments:
1 midterm exam, 8-12 homework assignments, several quizzes, and a final exam. |
Additional Requirements and/or Comments:
Some familiarity with programming in Python (or any programming language, for that matter) is very desirable but not required. |
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