结构化数据的概率模型课程详细信息

课程号 04833600 学分 2
英文名称 Probabilistic Models for Structured Data
先修课程 Pre-requisites: basic knowledge in statistics and probability, linear algebra, optimization, programming.

Target audience: Senior undergraduate students and graduate students in various disciplines (computer science, statistics, economics, finance, electronic engineering, biology, physics)  
中文简介 本课程旨在介绍结构化数据的概率模型,其中数据点(如顺序数据和图形/网络数据)不再彼此独立。 该课程将涵盖最前沿的概率模型的建模、推理和学习,包括隐马尔可夫模型,马尔可夫随机场,条件随机场和因子图,其应用跨越多个领域,如文本挖掘、医疗领域和社交网络分析。通过本课程的学习,学生有望获得:(1)理解适用于结构化数据的多个概率模型的数学表达式,包括其直观解释,数学推导和证明;(2)将这些模型应用于实际应用;(3)提出结构化数据的新模型的潜力。
英文简介 The course aims to introduce probabilistic models for structured data, where data points are no longer independent with each other, such as sequential data and graph/network data. The course will cover modeling, inference, and learning of state-of-the-art probabilistic models, including Hidden Markov Model, Markov Random Field, Conditional Random Field, and Factor Graph. Applications across different domains, such as text mining, medical domain, and social network analysis. At the end of the course, the students are expected to be able to do the following: (1) understanding the mathematical formulation of different probabilistic models that work for structured data, including intuition and mathematical derivations and proof; (2) apply these models to real-world applications; (3) potential of developing novel models for structured data for publications.
开课院系 信息科学技术学院
通选课领域  
是否属于艺术与美育
平台课性质  
平台课类型  
授课语言 英文
教材 Probabilistic Graphical Models,Daphne Koller and Nir Friedman,The MIT Press,2009;
An Introduction to Conditional Random Fields,Charles Sutton and Andrew McCallum,Now Publishers,2014,
参考书
教学大纲
Introduction to Probabilistic Models and Structured Data    3学时

Probabilistic Models for Unstructured Data    3学时

Warm up: Hidden Markov Models    3学时

Markov Random Fields    3学时

Gaussian Markov Random Fields    3学时

Hinge Loss Markov Random Fields    3学时

Conditional Random Fields    3学时

Skip-Chain Conditional Random Field    3学时

Factor Graph    3学时

Student Presentation/Exam    3学时
课堂授课为主,课后练习、讨论及汇报为辅。
Attendance and Discussions: 25%

Assignments: 45%

Exam: 30%
教学评估 曹永知:
学年度学期:17-18-3,课程班:结构化数据的概率模型1,课程推荐得分:4.69,教师推荐得分:4.69,课程得分分数段:95-100;