- 来自www.coursera.org
Probabilistic Graphical Models
课程
en
英语
165 时
此内容评级为 4.5/5

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课程详情
教学大纲
Topics covered include:
- 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
- Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
- Learning parameters and structure in PGMs
- 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:
- Credit Scoring and Factors
- Modeling Genetic Inheritance and Disease
- Markov Networks and Optical Character Recognition (OCR)
- Inference: Belief Propagation
- Markov Chain Monte Carlo and Image Segmentation
- Decision Theory: Arrhythmogenic Right Ventricular Dysplasia
- Conditional Random Field Learning for OCR
- Structure Learning for Identifying Skeleton Structure
- 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:- Introduction and Overview. Chapters 1, 2.1.1 - 2.1.4, 4.2.1.
- Bayesian Network Fundamentals. Chapters 3.1 - 3.3.
- Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
- Structured CPDs. Chapters 5.1 - 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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