|
課程名稱 |
統計通信理論
Statistical Communication Theory |
|
開課學期 |
99-2 |
|
授課對象 |
電機資訊學院 生醫電子與資訊學研究所 |
|
授課教師 |
陳光禎 |
|
課號 |
EE5005 |
|
課程識別碼 |
921 U0180 |
|
班次 |
|
|
學分 |
3 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期四6,7,8(13:20~16:20) |
|
上課地點 |
博理103 |
|
備註 |
總人數上限：47人 |
|
|
|
|
課程簡介影片 |
|
|
核心能力關聯 |
本課程尚未建立核心能力關連 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
.Contents:
1 Mathematical Background
1.1 Introduction to Probability Theory
1.2 Introduction to Mathematical Statistics
2 Statistical Decision Theory
3 Hypothesis Testing
4 Signal Detection
5 Parameter Estimation
6 Signal Estimation
7 Applications: Digital Transmission Theory
7.1 Continuous-time Detection and Estimation
7.2 Optimal Receiver
7.3 Inner Receiver Design
7.4 Multiuser Detection
8 Markov Decision Process
9 Game Theory
10 Statistical Inference*
11 Statistical Learning
11.1 Supervised Learning
11.2 Unsupervised Learning
12 Information Theory and Statistics*
13 Prediction Algorithms*
14 Applications: Statistical Bioinformatics
14.1 DNA Sequencing
14.2 Evolutionary Models
15 Cognitive Psychology*
15.1 Reasoning Systems
15.2 Belief and Propagation Networks
*Optional if time allows
|
|
課程目標 |
This course, as a new generation of statistical communication theory, intends to supply theoretical background regarding statistical decision theory and mathematical statistics at graduate level, which is useful in modern communication theory, information theory, pattern recognition, machine learning, and many advanced research such as biomedical applications, social networks, and cognition. Graduate/Undergraduate students who are interested in probability and probabilistic models are welcome. |
|
課程要求 |
Prerequisites: Probability, Signal and Systems (preferred but not required), Principles of
Communications (preferred but not required), Advanced Calculus (preferred but not
required), Detection and Estimation (preferred but not required)
Grades: Homework 20%
Mid-Term 20%
Final 30%
Term-Paper 30% (Proposal Due: ; Presentation: )
In IEEE T-IT paper format with 6-10 pages
|
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
Class note and selected papers |
|
參考書目 |
[1] J.O. Berger, Statistical Decision Theory, 1985.
[2] V. Poor, Introduction to Signal Detection and Estimation, 2nd edition, Springer.
[3] D.J.C. MacKay, Information Theory, Interference, and Learning Algorithms, Cambridge
University Press, 2003.
[4] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd edition,
Springer, 2009.
[5] M.L. Puterman, Markov Decision Processes, Wiley, 1994.
[6] N. Cesa-Bianchi, G. Lugosi, Prediction, Learning, and Games, Cambridge University Press, 2006.
[7] P.J. Bcikel, K.A. Doksum, Mathematical Statistics Vol. I Basic Ideas and Selected Topics, 2nd
edition, Pearson Education, 2007.
[8] S. Verdu, Multiuser Detection, Cambridge University Press, 1998.
[9] W. J. Ewens, G.R. Grant, Statistical Methods in Bioinformatics, 2nd edition, Springer, 2005.
[10]R.M. Gray, L.D. Davisson, An Introduction to Statistical Signal Processing, Cambridge
University Press, 2004. (update electronic version 2007)
|
|
評量方式
(僅供參考) |
|
|