| Date | Slides | Week and topic | Homework and logistics |
|---|---|---|---|
| 8/31🔴 No Class휴강 |
Conference Presentations 0. Student Research (full) 1. Select a Topic 2. Writing your Paper Conference Presentations 0. Student Research (full) 1. Select a Topic 2. Writing your Paper | ||
| 9/7 |
Week주차 1
Introduction
Introduction
| ||
| 9/14 |
Week주차 2
What is Information?
What is Information?
| Hello Python, MD | |
| 9/21 |
Week주차 3
Baye's Rule
Baye's Rule
| Baye's Rule | |
| 9/28 |
Week주차 4
Entropy
Entropy
| ||
| 10/5🔴 No Class휴강 |
No Class 추석!
No Class 추석!
| ||
| 10/12 |
Week주차 5
I: Data Compression - The Source Coding Theorem - Symbol Codes - Stream Codes I: Data Compression - The Source Coding Theorem - Symbol Codes - Stream Codes | Run huffman-coding.ipynb and submit file with outputs. | |
| 10/19 |
Week주차 6
II: Noisy-Channel Coding 8. Dependent Random Variables 9. Communication over a Noisy Channel 10. The Noisy-Channel Coding Theorem 11. Error-Correcting Codes and Real Channels II: Noisy-Channel Coding 8. Dependent Random Variables 9. Communication over a Noisy Channel 10. The Noisy-Channel Coding Theorem 11. Error-Correcting Codes and Real Channels | ||
| 10/26📝 Exam시험 |
Midterm Test Midterm Test | ||
| 11/2 |
Week주차 7
Entropy of Continuous Variables IV: Probabilities and Inference 20. A Example Inference Task: Clustering 21. Exact Inference by Complete Enumeration 22. Maximum Likelihood and Clustering 23. Useful Probability Distributions Entropy of Continuous Variables IV: Probabilities and Inference 20. A Example Inference Task: Clustering 21. Exact Inference by Complete Enumeration 22. Maximum Likelihood and Clustering 23. Useful Probability Distributions | ||
| 11/9 |
Week주차 8
Continuous Mutual Info, Channel Capacity
Continuous Mutual Info, Channel Capacity
| ||
| 11/16 |
Week주차 9
Rate Distortion Theory
Rate Distortion Theory
| ||
| 11/23 |
Week주차 10
Transfer Entropy, Thermodynamics
Transfer Entropy, Thermodynamics
| ||
| 11/30 |
Week주차 11
Information as Nature's Currency I
Information as Nature's Currency I
| ||
| 12/7 |
Week주차 12
Information as Nature's Currency II
Information as Nature's Currency II
| ||
| 12/14 |
Week주차 13
V: Neural Networks (Bonus) 38. Introduction to Neural Networks 39. The Single Neuron as a Classifier 40. Capacity of a Single Neuron V: Neural Networks (Bonus) 38. Introduction to Neural Networks 39. The Single Neuron as a Classifier 40. Capacity of a Single Neuron | ||
| 12/21📝 Exam시험 |
Final Test Final Test | ||
- No formal prerequisites. Curiosity required. 공식 선수 과목 없음. 호기심 필수.
정보이론의 기본 개념(엔트로피, 채널 용량, 코딩 이론)과 융합적 정보 전송 기술을 학습합니다.
This course covers information theory fundamentals — entropy, channel capacity, coding theory — and their applications in convergence communication systems.
-
★ Required교재Information Theory: A Tutorial IntroductionTutorial Introductions | 2015년 02월 01일Buy구매 → -
Supplementary참고Information Theory, Inference and Learning AlgorithmsCambridge University Press | 2003년 10월 06일Buy구매 → -
Supplementary참고Information Theory: From Coding to LearningCambridge University Press | 2024년 11월 30일Download (PDF)다운로드 (PDF) →
Aaron Snowberger earned his Ph.D. in Information and Communications Engineering from Hanbat National University in South Korea in 2023. He also holds degrees in Computer Science and Media Design. He has taught technology courses for over 8 years, English for over 15 years, and has freelanced as a web developer and magazine designer for over 5 years. His current research interests include computer vision, natural language processing, image processing, signal processing, and machine learning.
Aaron Snowberger는 2023년 한국 한밭대학교에서 정보통신공학 박사 학위를 취득했습니다. 그는 또한 컴퓨터 과학 및 미디어 디자인 학위를 취득했습니다. 그는 8년 이상 기술 과정을 가르쳤고, 15년 이상 영어를 가르쳤으며, 5년 이상 웹 개발자 및 잡지 디자이너로 프리랜서로 일했습니다. 현재 연구 관심사는 컴퓨터 비전, 자연어 처리, 영상 처리, 신호 처리, 기계 학습입니다.