Convergence Information Transmission

Grading성적 평가

Relative Grading상대평가 Grade distribution set by university policy.대학교 정책에 따라 성적 분포 결정.

20%Attend.출석

20%HW과제

30%Mid.중간

30%Final기말

20% Attendance출석20% Homework과제30% Midterm중간고사30% Final기말고사

Schedule강의 일정

8/25🔴 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 Template files
9/1	▶ Slides	Week주차 1 Introduction Introduction	Conference Presentation Info
9/8	▶ Slides	Week주차 2 What is Information? What is Information?	Hello Python, MD 과제 →과제 2 →
9/15	▶ Slides Slides 2	Week주차 3 Baye's Rule Baye's Rule	Baye's Rule 과제 →
9/22	▶ Slides Slides 2	Week주차 4 Entropy Entropy
9/29	▶ Slides Slides 2	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. 과제 →
10/6🔴 No Class휴강	No Class Chuseok!~ No Class Chuseok!~
10/13	▶ Slides	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	Join the Google Classroom
10/20📝 Exam시험	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
10/27📝 Exam시험	Midterm Test Study Guide Test A (Monday) Test B (Wednesday) Midterm Test Study Guide Test A (Monday) Test B (Wednesday) Not covered yet. IV: Probabilities and Inference 24. Exact Marginalization 27. Laplace's Method 28. Model Comparison and Occam's Razor 29. Monte Carlo Methods 30. Efficient Monte Carlo Methods 31. Ising Models 32. Exact Monte Carlo Sampling 33. Variational Methods 34. Independent Component Analysis and Latent Variable Modelling 35. Random Inference Topics
11/3	▶ Slides	Week주차 7 Continuous Mutual Info, Channel Capacity Continuous Mutual Info, Channel Capacity
11/10	▶ Slides	Week주차 8 Rate Distortion Theory Rate Distortion Theory
11/17	▶ Slides	Week주차 9 Transfer Entropy, Thermodynamics Transfer Entropy, Thermodynamics
11/24	▶ Slides	Week주차 10 Information as Nature's Currency I Information as Nature's Currency I
12/1	▶ Slides	Week주차 11 Information as Nature's Currency II Information as Nature's Currency II
12/8	▶ Slides	Week주차 12 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
12/15📝 Exam시험	Final Test Study Guide Test A (Monday) Test B (Wednesday) GitHub vs. Colab Survey Final Test Study Guide Test A (Monday) Test B (Wednesday) GitHub vs. Colab Survey

Overview과목 소개

Prerequisites선수 과목

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.

Textbooks교재

★ Required교재
Information Theory, Inference and Learning Algorithms

MacKay, David J. C.

Cambridge University Press | 2003년 10월 06일
Buy구매 →
Supplementary참고
Information Theory: From Coding to Learning

Polyanskiy, Yury & Wu, Yihong

Cambridge University Press | 2024년 11월 30일
Buy구매 →
Supplementary참고
Information Theory: A Tutorial Introduction

Stone, James V.

Tutorial Introductions | 2015년 02월 01일
Buy구매 →

Instructor강사 소개

Aaron Snowberger

Ph.D. · Hanbat National University (2023)

✉ aaronkr.trainer @ gmail.com 💬 KakaoTalk: AaronKr 🌐 aaron.kr 🔬 PAI Lab →🆔 ORCID ⌨ GitHub

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.