課程名稱 |
消息理論 Information Theory |
開課學期 |
108-1 |
授課對象 |
電機資訊學院 電機工程學研究所 |
授課教師 |
王奕翔 |
課號 |
EE5028 |
課程識別碼 |
921 U1190 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期三2,3,4(9:10~12:10) |
上課地點 |
電二106 |
備註 |
總人數上限:50人 |
課程網頁 |
https://cool.ntu.edu.tw/courses/400 |
課程簡介影片 |
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核心能力關聯 |
本課程尚未建立核心能力關連 |
課程大綱
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課程概述 |
Information Theory is a senior (undergraduate) level course designed for students who are interested in the quantitative fundamental limits of information. What is information and how to quantify information? What is the fundamental limits in representing information, delivering information, and learning information? In this course, we first introduce the fascinating theory originated from Claude E. Shannon, which addresses the above fundamental questions in the context of communication systems. We will also introduce algorithms that achieve these fundamental limits. Finally, we will demonstrate the application of information theory to other fields, including statistical inference, sparse recovery, stochastic optimization, and machine learning. |
課程目標 |
1. Introduce the fundamental limits in representing information (source coding), protecting information (channel coding), and learning information (statistical inference), along with the fundamental measures of information.
2. Develop algorithms to achieve these fundamental limits.
3. Demonstrate applications of information theory in communications, signal processing, and machine learning.
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課程要求 |
Prerequisite: Probability, Linear Algebra
Grading: Homework (40+5%), Exam (30%), Project (25%)
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
Lectures will be based on lecture notes and slides. Further information about assigned readings will be provided in the first lecture. |
參考書目 |
1. T. Cover and J. Thomas, Elements of Information Theory, Second Edition, Wiley-Interscience, 2006.
2. R. Gallager, Information Theory and Reliable Communications, Wiley, 1968.
3. I. Csiszar and J. Korner, Information Theory: Coding Theorems for Discrete Memoryless Systems, Second Edition, Cambridge University Press, 2011.
4. S. M. Moser, Information Theory (Lecture Notes), 6th Edition, ISI Lab, ETH Zürich, Switzerland, 2018.
5. Y. Polyanskiy and Y. Wu, Lecture notes on Information Theory, MIT (6.441), UIUC (ECE 563), Yale (STAT 664), 2012-2017. |
評量方式 (僅供參考) |
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