1. |
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Course Introduction |
강의 소개 |
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Artificial Intelligence. Part1 |
AI 개요 |
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Artificial Intelligence. Part2 |
history and issues |
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Machine learning. Part1 |
Introduction to Machine Learning |
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Machine learning. Part2 |
ML components, data and approaches |
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Machine learning. Part3 |
Applications and prerequisite |
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2. |
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Linear Algebra Review. Part1 |
Calculus for ML |
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Linear Algebra Review. Part2 |
Linear Algebra: Vector |
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Linear Algebra Review. Part3 |
Linear Algebra: Matrix |
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Linear Algebra Review. Part4 |
Linear Algebra: Decompsition and Derivatives |
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3. |
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Probability Review. Part1 |
Probability and statistics |
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Probability Review. Part2 |
Bayes Theorem |
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Probability Review. Part3 |
Gaussian and other distributions |
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4. |
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Information Theory. Part1 |
Information theory, entropy |
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Information Theory. Part2 |
KL, Mutual Information, cross entropy |
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5. |
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Density estimation. Part1 |
density estimation, parametric method |
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Density estimation. Part2 |
non-parametric method and semi-parametric method |
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6. |
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Decision Theory. Part1 |
classification 관련 decision theory |
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Decision Theory. Part2 |
classification 관련 decision theory |
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Decision Theory. Part3 |
classification 관련 decision theory |
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7. |
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Clustering. Part1 |
introduction to clustering |
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Clustering. Part2 |
kMeans Algorithm |
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Clustering. Part3 |
mixture of Gaussian |
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Clustering. Part4 |
EM algorithm |
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Clustering. Part5 |
EM application |
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8. |
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Dimension reduction. Part1 |
차원 축소를 위한 선형 알고리즘 |
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Dimension reduction. Part2 |
차원 축소를 위한 선형 알고리즘 |
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Dimension reduction. Part3 |
차원 축소를 위한 선형 알고리즘 |
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Dimension reduction. Part4 |
차원 축소를 위한 선형 알고리즘 |
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Dimension reduction. Part5 |
차원 축소를 위한 선형 알고리즘 |
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9. |
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Nonlinear Dimension Reduction. Part1 |
kernel machines and manifold learning |
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Nonlinear Dimension Reduction. Part2 |
kernel machines and manifold learning |
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Nonlinear Dimension Reduction. Part3 |
kernel machines and manifold learning |
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Nonlinear Dimension Reduction. Part4 |
kernel machines and manifold learning |
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10. |
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Classification. Part1 |
분류 기초 및 관련 알고리즘 |
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Classification. Part2 |
k nearest neighbor |
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Classification. Part3 |
Na�ve Bayes Classifier |
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Classification. Part4 |
Decision tree part1 |
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Classification. Part5 |
Decision tree part2 |
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11. |
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Ensemble Learning. Part1 |
ensemble learning 기초 |
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Ensemble Learning. Part2 |
why and how it works |
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12. |
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Regression. Part1 |
회귀분석 모델 |
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Regression. Part2 |
회귀분석 모델 |
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Regression. Part3 |
회귀분석 모델 |
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Regression. Part4 |
회귀분석 모델 |
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13. |
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Neural Networks. Part1 |
신경망 소개와 역사 |
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Neural Networks. Part2 |
forward propagation |
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Neural Networks. Part3 |
backward propagation |
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Neural Networks. Part4 |
training and properties |
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14. |
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Optimization. Part1 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part2 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part3 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part4 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part5 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part6 |
최적화(gradient descent, Networn method, Trust-region) |
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Optimization. Part7 |
최적화(gradient descent, Networn method, Trust-region) |
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15. |
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Regularization. Part1 |
overfitting 과 regularization 이해 |
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Regularization. Part2 |
overfitting 과 regularization 이해 |
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Deep Learning. Part1 |
딥러닝 소개 |
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Deep Learning. Part2 |
neuroscientific support and early DL |
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Deep Learning. Part3 |
DL algorithms |
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Recommendation. Part1 |
추천 알고리즘 소개 |
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Recommendation. Part2 |
추천 알고리즘 소개 |
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Recommendation. Part3 |
추천 알고리즘 소개 |
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SVM. Part1 |
SVM 설명 |
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SVM. Part2 |
SVM 설명 |
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SVM. Part3 |
SVM 설명 |
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HMM. Part1 |
HMM 설명 |
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HMM. Part2 |
3 problems in HMM |
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