NTU-Coursera机器学习:機器學習技法 (Machine Learning Techniques)
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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。]
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.
- 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)
- 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)
- 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)
- 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.)
- 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.)
- Matrix Factorization Techniques for Recommender Systems (Koren et al.)
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