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The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。
Syllabus :
1. 第一講：Linear Support Vector Machine
- Large-Margin Separating Hyperplane
- Standard Large-Margin Problem
- Support Vector Machine
- Reasons behind Large-Margin Hyperplane
2. 第二講：Dual Support Vector Machine
- Motivation of Dual SVM
- Lagrange Dual SVM
- Solving Dual SVM
- Messages behind Dual SVM
3. 第三講：Kernel Support Vector Machine
- Kernel Trick
- Polynomial Kernel
- Gaussian Kernel
- Comparison of Kernels
4. 第四講：Soft-Margin Support Vector Machine
- Motivation and Primal Problem
- Dual Problem
- Messages behind Soft-Margin SVM
- Model Selection
5. 第五講：Kernel Logistic Regression
- Soft-Margin SVM as Regularized Model
- SVM versus Logistic Regression
- SVM for Soft Binary Classification
- Kernel Logistic Regression
6. 第六講：Support Vector Regression
- Kernel Ridge Regression
- Support Vector Regression Primal
- Support Vector Regression Dual
7. 第七講：Blending and Bagging
- Motivation of Aggregation
- Uniform Blending
- Linear and Any Blending
- Bagging (Bootstrap Aggregation)
8. 第八講：Adaptive Boosting
- Motivation of Boosting
- Diversity by Re-weighting
- Adaptive Boosting Algorithm
- Adaptive Boosting in Action
9. 第九講：Decision Tree
- Decision Tree Hypothesis
- Decision Tree Algorithm
- Decision Tree Heuristics in C&RT
- Decision Tree in Action
10. 第十講：Random Forest
- Random Forest Algorithm
- Out-Of-Bag Estimate
- Feature Selection
- Random Forest in Action
11. 第十一講：Gradient Boosted Decision Tree
- Adaptive Boosted Decision Tree
- Optimization View of AdaBoost
- Gradient Boosting
- Summary of Aggregation Models
12. 第十二講：Neural Network
- Neural Network Hypothesis
- Neural Network Learning
- Optimization and Regularization
13. 第十三講：Deep Learning
- Deep Neural Network
- Autoencoder
- Denoising Autoencoder
- Principal Component Analysis
14. 第十四講：Radial Basis Function Network
- RBF Network Hypothesis
- RBF Network Learning
- k-Means Algorithm
- k-Means and RBF Network in Action
15. 第十五講：Matrix Factorization
- Linear Network Hypothesis
- Basic Matrix Factorization
- Stochastic Gradient Descent
16. 第十六講：Finale
- Feature Exploitation Techniques
- Error Optimization Techniques
- Overfitting Elimination Techniques
- Machine Learning in Practice
