100年第1學期-4434 人工智慧方法論 課程資訊

評分方式

評分項目	配分比例	說明
Homework and labs	20
Midterm	30
Final project	30
Attendence and paticipation	20

教育目標

This course is designed for doctorate and advanced master level students. The purpose of this course is to provide an overview and inside methodologies of the Bayesian Networks. Although the concepts and the applications are going to be the major contents, the basic theoretical results will be covered along the couse if needed. In this course, the students have to read and present materials assigned in the lectures, to work the homework assignments with/without using the software Netica.

課程概述

This course is designed for the Ph.D. or aggresive Master students. The purpose of this course is to help the students to have acquaintance with the theoretical concepts and methodologies in the Statistical Learning Theory (SLT). The ways to expolore the relationships among data attracts many research attentions in these years. SLT is a heavy investigated, both in theory and methodologies, alternative in pursuing the target.

基本資料

選修課，學分數：3-0
上課時間：二/2,3,4[C104]
修課班級：工工碩博
修課年級：年級以上
選課備註：B637

參考書目

1. Koller, D. and N. Friedman, Probabilistic Graphicl Models – Principles and Techniques, The MIT Press, 2009
2. Neapolitan, Richard E., Learning Bayesian Networks, Prentice Hall, 2004
3. Jensen, F.V., Bayesian Networks and Decision Graphs, Springer, 2001
4. Pearl, Judea, Causality: Models, Reasoning, and Inference, Cambridge, 2000
5. Jordan, Michael I, Learning in Graphical Models, MIT Press, 1999