100年第1學期-4434 人工智慧方法論 課程資訊
|Homework and labs||20|
|Attendence and paticipation||20|
本課程名額為 70人，已有34 人選讀，尚餘名額36人。
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.
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.
Office HourFri 8:30-10:30 am
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