103學年度下學期 - 最佳化理論

最佳化理論

OPTIMIZATION THEORY

ICE508

講授類

選修

通訊工程研究所碩士班

選修

溫朝凱副教授

1. Statistical Learning
2. Linear Regression
3. Classification
4. Resampling Methods
5. Linear Model Selection and Regularization
6. Moving Beyond Linearity
7. Tree-Based Methods
8. Support Vector Machines
9. Unsupervised Learning

With the explosion of "Big Data" problems, optimization theory has found applications in such hot field. The last decade has seen a significant expansion of the number of possible approaches. In this course, we aim to give students in-depth tutorial to such modern methods, and the training to use a statistics package to solve real business situations.

Lecture

1.Quiz：30%
2.Midterm exam：30%
3.Final Project：40%

序號	作者	書名	出版社	出版年	出版地	ISBN#
1	G. James, D. Witten, T. Hastie and R. Tibshirani	An Introduction to Statistical Learning with Applications in R	Springer	2014		978-1461471370
2	S. Boyd and L. Vandenberghe	Convex Optimization	Cambridge Univ. Press	2004		007-125579-6

週次	日期	授課內容及主題
1	2015/02/25~2015/03/01	Statistical Learning
2	2015/03/02~2015/03/08	Statistical Learning
3	2015/03/09~2015/03/15	Linear Regression
4	2015/03/16~2015/03/22	Classification
5	2015/03/23~2015/03/29	Classification
6	2015/03/30~2015/04/05	Classification
7	2015/04/06~2015/04/12	Resampling Methods
8	2015/04/13~2015/04/19	Linear Model Selection and Regularization
9	2015/04/20~2015/04/26	Linear Model Selection and Regularization
10	2015/04/27~2015/05/03	Midterm
11	2015/05/04~2015/05/10	Robust estimation
12	2015/05/11~2015/05/17	Moving Beyond Linearity
13	2015/05/18~2015/05/24	Tree-Based Methods
14	2015/05/25~2015/05/31	Tree-Based Methods
15	2015/06/01~2015/06/07	Support Vector Machines
16	2015/06/08~2015/06/14	Unsupervised Learning
17	2015/06/15~2015/06/21	Final Presenations
18	2015/06/22~2015/06/28	Final Presenations

時段1:
時間：星期四12:00~14:00
地點：工EC9009
時段2：
時間：星期五12:00~14:00
地點：工EC9009

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工程技術導論與專題實務創新課程模組

103學年度下學期 - 最佳化理論