3. 강의목표
This course offers an introduction to the information theory and its applications to reliable and efficient information systems.
1. Understand concepts such as entropy, divergence, mutual information;
2. Understand lossless data compression and lossy data compression;
3. Understand channel capacity and reliable data transmission;
4. Understand connections between information theory and machine learning.
5. 성적평가
Homework: 20%
Class participation: 10%
Midterm exam: 30%
Final exam: 40%
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
Elements of Information Theory (2nd ed.)
|
T. Cover & J. Thomas
|
Wiley
|
2005
|
978-0471241959
|
8. 강의진도계획
Week 1
Introduction and entropy
Week 2
Divergence, mutual information, and Fano’s inequality
Week 3
AEP and entropies of stochastic process
Week 4
Data compression and Huffman codes
Week 5
Lossless compression
Week 6
Channel capacity
Week 7
Channel coding theorem
Week 8
Mid-term exam
Week 9
Strong coding theorem and error exponents
Week 10
Joint source channel coding
Week 11
Differential entropy and entropy maximization
Week 12
Additive Gaussian noise channel
Week 13
Parallel Gaussian channels and water-filling
Week 14
Rate distortion theory and Blahut-Arimoto algorithm
Week 15
Applications (machine learning, etc.)
Week 16
Final exam
10. 학습법 소개 및 기타사항
정보이론의 중요한 개념들이 통신, 신호처리, 기계학습, 인공지능 분야에 어떻게 적용되는지에 대해 강의할 예정입니다.
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청