출처:
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머신러닝 교과서 with 파이썬, 사이킷런, 텐서플로
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Textbook of Machine Learning
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혼자 공부하는 머신러닝 + 딥러닝
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기계학습입문(SKKU, 김재광 교수님)
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Coursera: Machine Learning (Prof Andrew Ng)
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CS229
- Linear Algebra
- Probability Theory
- Linear Regression
- Locally Weighted & Logistic Regression
- Perceptron & Generalized Linear Model
- GDA & Naive Bayes
- Support Vector Machines
- Kernels
- Data Splits, Models & Cross-Validation
- Approx/Estimation Error & ERM
- Decision Trees and Ensemble Methods
- Neural Networks
- Backprop & Improving Neural Networks
- Debugging ML Models and Error Analysis
- Expectation-Maximization Algorithms
- EM Algorithm & Factor Analysis
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Andrew Ng Problem Sets:
- Set0 (2022/01)
- Set1 (2022/07/29~2022/08/14)
- Set2 (2022/08/14~2022/09/16)
- Set3 (2022/11/06~)
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Materials/Linear Algebra: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
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Materials/Probability Theory: MIT