Probabilistic Graphical Models

课程
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英语
165 时
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  • 来自www.coursera.org
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课程详情

教学大纲

Topics covered include:

  1. The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities
  2. Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
  3. Learning parameters and structure in PGMs
  4. Using a PGM for decision making under uncertainty.

There will be short weekly review quizzes and programming assignments (Octave/Matlab) focusing on case studies and applications of PGMs to real-world problems:

  1. Credit Scoring and Factors
  2. Modeling Genetic Inheritance and Disease
  3. Markov Networks and Optical Character Recognition (OCR)
  4. Inference: Belief Propagation
  5. Markov Chain Monte Carlo and Image Segmentation
  6. Decision Theory: Arrhythmogenic Right Ventricular Dysplasia
  7. Conditional Random Field Learning for OCR
  8. Structure Learning for Identifying Skeleton Structure
  9. Human Action Recognition with Kinect

To prepare for the class in advance, you may consider reading through the following sections of the 

 by Daphne and Nir Friedman:

  1. Introduction and Overview. Chapters 1, 2.1.1 - 2.1.4, 4.2.1.
  2. Bayesian Network Fundamentals. Chapters 3.1 - 3.3.
  3. Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
  4. Structured CPDs. Chapters 5.1 - 5.5.
  5. Template Models. Chapters 6.1 - 6.4.1.

These will be covered in the first two weeks of the online class.

The slides for the whole class can be found 

.

先决条件

没有。

讲师

  • Daphne Koller - School of Engineering

编辑

利兰-斯坦福大学(Leland Stanford Junior University),简称斯坦福大学,是一所美国私立大学,位于旧金山南部的硅谷。

其校训是 "Die Luft der Freiheit weht",意为 "自由之风拂面"。

在大多数国际排名中,斯坦福大学都名列世界顶尖大学之列,享有极高的声誉。

平台

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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