Python Machine Learning

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	Hello Python 과제 →
9/8	▶ Slides	Week주차 2 Class Overview, HW submission, and Git Basics Chapter 0. Environment and Mathematical Preliminaries Class Overview, HW submission, and Git Basics Chapter 0. Environment and Mathematical Preliminaries	Env & Math 과제 →과제 2 →
9/15	▶ Slides	Week주차 3 Part I: Data is Everything Chapter 1. It's all about the Data Chapter 2. Building the Datasets Part I: Data is Everything Chapter 1. It's all about the Data Chapter 2. Building the Datasets	Data Data Data 과제 →
9/22	▶ Slides	Week주차 4 Part II: Classical Machine Learning Chapter 3. Introduction to Machine Learning Chapter 4. Experiments with Classical Models Part II: Classical Machine Learning Chapter 3. Introduction to Machine Learning Chapter 4. Experiments with Classical Models	Classic ML 과제 →
9/29	▶ Slides Slides 2	Week주차 5 Part III: Neural Networks Chapter 5. Introduction to Neural Networks Chapter 6. Training a Neural Network Part III: Neural Networks Chapter 5. Introduction to Neural Networks Chapter 6. Training a Neural Network	MNIST NN Fashion MNIST NN 과제 →
10/6🔴 No Class휴강	No Class Chuseok!~ No Class Chuseok!~
10/13	▶ Slides	Week주차 6 Part III: Neural Networks Chapter 7. Experiments with Neural Networks Part III: Neural Networks Chapter 7. Experiments with Neural Networks	Join the Google Classroom (Gmail)
10/20	▶ Slides	Week주차 7 Part III: Neural Networks Chapter 8. Evaluating Models Part III: Neural Networks Chapter 8. Evaluating Models
10/27📝 Exam시험	Midterm Test Study Guide Midterm Project Due: Nov 9, 2025 0:00 Midterm Test A Midterm Test B Midterm Test Study Guide Midterm Project Due: Nov 9, 2025 0:00 Midterm Test A Midterm Test B
11/3	▶ Slides Slides 2	Week주차 8 Part IV: Convolutional Neural Networks Chapter 9. Introduction to Convolutional Neural Networks Part IV: Convolutional Neural Networks Chapter 9. Introduction to Convolutional Neural Networks	Visualizing CNN Training
11/10	▶ Slides	Week주차 9 Part IV: Convolutional Neural Networks Chapter 10. Experiments with Keras and MNIST Chapter 11. Experiments with CIFAR-10 Chapter 12. A Case Study: Classifying Audio Samples Part IV: Convolutional Neural Networks Chapter 10. Experiments with Keras and MNIST Chapter 11. Experiments with CIFAR-10 Chapter 12. A Case Study: Classifying Audio Samples	ESC-50 Dataset for Audio Classification
11/17	▶ Slides Slides 2	Week주차 10 Part V: Advanced Networks and Generative AI Chapter 13. Advanced CNN Architectures Chapter 14. Fine-Tuning and Transfer Learning ~~Chapter 15. From Classification to Localization~~ Part V: Advanced Networks and Generative AI Chapter 13. Advanced CNN Architectures Chapter 14. Fine-Tuning and Transfer Learning ~~Chapter 15. From Classification to Localization~~	과제 →
11/24	▶ Slides	Week주차 11 Part V: Advanced Networks and Generative AI Chapter 16. Self-Supervised Learning Part V: Advanced Networks and Generative AI Chapter 16. Self-Supervised Learning
12/1	▶ Slides	Week주차 12 Part V: Advanced Networks and Generative AI RNNs, Attention, & Transformers Part V: Advanced Networks and Generative AI RNNs, Attention, & Transformers
12/8	▶ Slides	Week주차 13 Part V: Advanced Networks and Generative AI Chapter 18. Large Language Models (LLMs) and Transformers Part V: Advanced Networks and Generative AI Chapter 18. Large Language Models (LLMs) and Transformers
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

Python programming; linear algebra; basic statistics 파이썬 프로그래밍; 선형대수; 기초 통계

Python을 활용한 기계학습 및 딥러닝의 핵심 개념과 실무 적용 방법을 학습합니다. scikit-learn, PyTorch 등 주요 라이브러리를 사용한 실습 중심의 과목입니다.

This course covers machine learning and deep learning fundamentals using Python. Students gain hands-on experience with scikit-learn and PyTorch, building practical ML models from data to deployment.

Textbooks교재

★ Required교재
Practical Deep Learning, 2nd Edition

Ronald T. Kneusel

No Starch Press | 2025년 07월 08일
Buy구매 →
Supplementary참고
Deep Learning: A Visual Approach

Andrew Glassner

No Starch Press | 2021년 06월 29일
Buy구매 →
Supplementary참고
Deep Learning

Ian Goodfellow, Yoshua Bengio, & Aaron Courville

MIT Press | 2016년 11월 18일
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.