🎓 Machine Learning and Optimization – YouTube Lecture Notes

이 저장소는 Machine Learning and Optimization (머신러닝과 최적화) 유튜브 강의의 보조 학습 자료를 제공합니다.
This repository contains supplementary lecture notes (PDF) for the Machine Learning and Optimization YouTube lecture series.

강의는 머신러닝의 기본 개념과 핵심 수식을 수학적으로 다루며, 최적화 이론까지 함께 설명합니다.
The lectures cover foundational concepts and mathematical formulations in machine learning, with an introduction to optimization techniques.

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📺 유튜브 강의 링크 / YouTube Video Link

• 👉 강의 보러 가기 / Watch the lecture series

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📄 강의 노트 / Lecture Notes

• 주요 개념, 수식, 예제, 시각 자료가 포함된 PDF입니다.
  This PDF includes major concepts, mathematical formulas, examples, and visual aids.

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🗂️ 강의 목차 / Lecture Schedule

주차 / Week	주제 / Topics
✅ 1주차 / Week 1	Linear Regression, Ridge Regression, SVD, Bias-Variance Trade Off
✅ 2주차 / Week 2	Model Selection and Validation, Hoeffding's Inequality
✅ 3주차 / Week 3	Convex Optimization, Gradient Descent
✅ 4주차 / Week 4	Stochastic Gradient Descent
✅ 5주차 / Week 5	Logistic Regression
✅ 6주차 / Week 6	Statistical Learning Theory, Algorithm Stability
✅ 7주차 / Week 7	Algorithmic Regularization, Early Stopping
✅ 8주차 / Week 8	Support Vector Machine (SVM), Nonlinear Features and Kernels
✅ 9주차 / Week 9	Neural Networks
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✅ 10주차 / Week 10	Sparse Reconstruction
✅ 11주차 / Week 11	Invervse Problems as optimization tasks
✅ 12주차 / Week 12	Learning to solve inverse problems end-to-end

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📬 문의 및 피드백 / Contact & Feedback

• 질문이나 제안은 Issues로 남겨주세요.
  Leave questions or feedback on the Issues page.
• 강의 및 노트는 지속적으로 업데이트될 예정입니다.
  More content will be added and updated continuously.