NTU-Coursera机器学习:機器學習技法 (Machine Learning Techniques)

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。]

Course Syllabus

Each of the following items correspond to approximately one hour of video lecture. [以下的每個小項目對應到約一小時的線上課程]
Embedding Numerous Features [嵌入大量的特徵]
-- Linear Support Vector Machine [線性支持向量機]
-- Dual Support Vector Machine [對偶支持向量機]
-- Kernel Support Vector Machine [核型支持向量機]
-- Soft-Margin Support Vector Machine [軟式支持向量機]
-- Kernel Logistic Regression [核型羅吉斯迴歸]
-- Support Vector Regression [支持向量迴歸]
Combining Predictive Features [融合預測性的特徵]
-- Bootstrap Aggregation [自助聚合法]
-- Adaptive Boosting [漸次提昇法]
-- Decision Tree [決策樹]
-- Random Forest [隨機森林]
-- Gradient Boosted Decision Tree [梯度提昇決策樹]
Distilling Hidden Features [萃取隱藏的特徵]
-- Neural Network [類神經網路]
-- Deep Learning [深度學習]
-- Radial Basis Function Network [逕向基函數網路]
-- Matrix Factorization [矩陣分解]

Summary [總結]

延伸閱讀

先修書籍

• Learning from Data: A Short Course , Abu-Mostafa, Magdon-Ismail, Lin, 2013.

參考文獻

201, 202, 203, 204：

• Learning from Data e-Chapter 8: Support Vector Machine, 可由 http://book.caltech.edu/bookforum/ 免費下載(帳號：mooc 密碼： massive)
• A training algorithm for optimal margin classifiers. Boser, Guyon, Vapnik, COLT 1992.

205, 206：

• Kernel Logistic Regression and the Import Vector Machine . Zhu, Hastie, NIPS 2001.
• Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Platt, 1999.
• A Note on Platt's Probabilistic Outputs for Support Vector Machines. Lin, Lin, Weng, MLJ 2007.
• SVM versus Least Squares SVM (Ye and Xiong)
• A Tutorial on Support Vector Regression (Smola and Scholkopf)

207, 208：

• A linear ensemble of individual and blended models for music rating prediction (Chen et al.)
  
• Bagging predictors (Breiman)
• A short introduction to boosting (Freund and Schapire)

209, 210, 211：

• Classification and regression trees (overview of decision tree by Loh)
• Classification and regression trees (book of CART by Breiman et al.)
• Random forest (Breiman)
• Greedy Function Approximation: A Gradient Boosting Machine (Friedman)

212, 213：

• Learning from Data e-Chapter 7: Neural Networks, 可由 http://book.caltech.edu/bookforum/ 免費下載(帳號：mooc 密碼： massive)
• Stacked Denoising Autoencoders: Learning Useful Representations ina Deep Network with a Local Denoising Criterion (Vincent et al.)

214：

• Learning from Data e-Chapter 6: Similarity Models, 可由 http://book.caltech.edu/bookforum/ 免費下載(帳號：mooc 密碼： massive)
• Three Learning Phases for Radial-basis-function Networks (Schwenker et al.)

215：

• Matrix Factorization Techniques for Recommender Systems (Koren et al.)

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