NTU-Coursera机器学习:机器学习基石 (Machine Learning Foundations)

课讲内容

这门课以8周设计,分成 4个核心问题,每个核心问题约需2周的时间来探讨.每个约2个小时的录影中,每个小时为一个主题,以会各分成4到5个小段落,每个段落里会有一个后多个随堂的练习.我们在探讨每个核心问题的第二周。依上所述，課程的規畫如下：

When Can Machines Learn? [何时可以使用机器学习]

• 第一周:(NTU-Coursera机器学习:机器学习问题与二元分类 )
  第一讲：The Learning Problem [机器学习问题]
  第二讲：Learning to Answer Yes/No [二元分类]
• 第二周: 
  作业一: (NTU-Coursera机器学习:HomeWork 1 Q15-20)
  第三讲：Types of Learning [各式機器學習問題] (NTU-Coursera机器学习:Types of Learning )
  第四讲：Feasibility of Learning [机器学习的可行性] (NTU-Coursera机器学习:机器学习的可行性 & 训练与测试 )
  

Why Can Machines Learn? [为什么机器可以学习]

• 第三週:
  第五讲：Training versus Testing [训练与测试] (NTU-Coursera机器学习:机器学习的可行性 & 训练与测试 )
  第六讲：Theory of Generalization [举一反三的一般化理论]
• 第四週: 
  作业二: (NTU-Coursera机器学习:HomeWork 2 Q16-20) 
  第七讲：The VC Dimension (NTU-Coursera机器学习:VC Bound和VC维度 )
  第八讲：Noise and Error (NTU-Coursera机器学习:Noise and Error )

How Can Machines Learn? [机器可以怎么样学习]

• 第五週: 
  第九讲：Linear Regression [線性迴歸] 
  第十讲：`Soft‘ Classification [軟性分類]
• 第六週:
  十一讲：Multiclass Classification [多類別分類] 
  十二讲：Nonlinear Transformation [非線性轉換]

How Can Machines Learn Better? [機器可以怎麼樣學得更好]

• 第七週: 
  十三讲：Hazard of Overfitting [過度訓練的危險] 
  十四讲：Regularization [探制調適]
• 第八週:
  十五讲：Validation [自我檢測] 
  十六讲：Three Learning Principles [三個機器學習的重要原則]

延伸阅读

預备知识

• 作业零 (機率統計、線性代數、微分之基本知識)

参考书籍

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

经典文献

• F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386-408, 1958. (第二講：Perceptron 的出處)

• W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13–30, 1963. (第四講：Hoeffding‘s Inequality)
  

• Y. S. Abu-Mostafa, X. Song , A. Nicholson, M. Magdon-ismail. The bin model, 1995. (第四講：bin model 的出處)

• V. Vapnik. The nature of statistical learning theory, 2nd edition, 2000. (第五到八講：VC dimension 與 VC bound 的完整數學推導及延伸)

• Y. S. Abu-Mostafa. The Vapnik-Chervonenkis dimension: information versus complexity in learning. Neural Computation, 1(3):312-317, 1989. (第七講：VC Dimension 的概念與重要性)
  

參考文獻

• A. Sadilek, S. Brennan, H. Kautz, and V. Silenzio. nEmesis: Which restaurants should you avoid today? First AAAI Conference on Human Computation and Crowdsourcing, 2013. (第一講：ML 在「食」的應用)

• Y. S. Abu-Mostafa. Machines that think for themselves. Scientific American, 289(7):78-81, 2012. (第一講：ML 在「衣」的應用)

• A. Tsanas, A. Xifara. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49: 560-567, 2012. (第一講：ML 在「住」的應用)

• J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel. Introduction to the special issue on machine learning for traffic sign recognition. IEEE Transactions on Intelligent Transportation Systems 13(4): 1481-1483, 2012. (第一講：ML 在「行」的應用)

• R. Bell, J. Bennett, Y. Koren, and C. Volinsky. The million dollar programming prize. IEEE Spectrum, 46(5):29-33, 2009. (第一講：Netflix 大賽)

• S. I. Gallant. Perceptron-based learning algorithms. IEEE Transactions on Neural Networks, 1(2):179-191, 1990. (第二講：pocket 的出處，注意到實際的 pocket 演算法比我們介紹的要複雜)

• R. Xu, D. Wunsch II. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645-678, 2005. (第三講：Clustering)

• X. Zhu. Semi-supervised learning literature survey. University of Wisconsin Madison, 2008. (第三講：Semi-supervised)

• Z. Ghahramani. Unsupervised learning. In Advanced Lectures in Machine Learning (MLSS ’03), pages 72–112, 2004. (第三講：Unsupervised)
  

• L. Kaelbling, M. Littman, A. Moore. reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4: 237-285. (第三講：Reinforcement)

• A. Blum. On-Line algorithms in machine learning. Carnegie Mellon University,1998. (第三講：Online)

• B. Settles. Active learning literature survey. University of Wisconsin Madison, 2010. (第三講：Active)

• D. Wolpert. The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7): 1341-1390. (第四講：No free lunch 的正式版)

• T. M. Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 14(3):326–334, 1965. (第五到六講：Growth Function)
  

• B. Zadrozny, J. Langford, N. Abe. Cost sensitive learning by cost-proportionate example weighting. IEEE International Conference on Data Mining, 2003. (第八講：Weighted Classification)

• G. Sever, A. Lee. Linear Regression Analysis, 2nd Edition, Wiley, 2003. (第九講：Linear Regression 由統計學的角度來分析；第十二到十三講：Polynomial Transform 後再做 Linear Regression)

• D. C. Hoaglin, R. E. Welsch. The hat matrix in regression and ANOVA. American Statistician, 32:17–22, 1978. (第九講：Linear Regression 的 Hat Matrix)
  

• D. W. Hosmer, Jr., S. Lemeshow, R. X. Sturdivant. Applied Logistic Regression, 3rd Edition, Wiley, 2013 (第十講：Logistic Regression 由統計學的角度來分析)

• T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. International Conference on Machine Learning,  (第十一講：Stochastic Gradient Descent 用在線性模型的理論分析)

• R. Rifkin, A. Klautau. In Defense of One-Vs-All Classification. Journal of Machine Learning Research, 5: 101-141, 2004. (第十一講：One-versus-all)
  

• J. Fürnkranz. Round Robin Classification. Journal of Machine Learning Research, 2: 721-747, 2002. (第十一講：One-versus-one)

• L. Li, H.-T. Lin. Optimizing 0/1 loss for perceptrons by random coordinate descent. In Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN ’07), pages 749–754, 2007. (第十一講：一個由最佳化角度出發的 Perceptron Algorithm)

• G.-X. Yuan, C.-H. Ho, C.-J. Lin. Recent advances of large-scale linear classification. Proceedings of IEEE, 2012. (第十一講：更先進的線性分類方法)

• Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, C.-J. Lin. Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 11(2010), 1471-1490. (第十二講：一個使用多項式轉換加上線性分類模型的方法)

• M. Magdon-Ismail, A. Nicholson, Y. S. Abu-Mostafa. Learning in the presence of noise. In Intelligent Signal Processing. IEEE Press, 2001. (第十三講：Noise 和 Learning)

• A. Neumaier, Solving ill-conditioned and singular linear systems: A tutorial on regularization, SIAM Review 40 (1998), 636-666. (第十四講：Regularization)
  

• T. Poggio, S. Smale. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society, 50(5):537–544, 2003. (第十四講：Regularization)
  

• P. Burman. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3): 503–514, 1989. (第十五講：Cross Validation)

• R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial intelligence (IJCAI ’95), volume 2, 1137–1143, 1995. (第十五講：Cross Validation)
  

• A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Occam’s razor. Information Processing Letters, 24(6):377–380, 1987. (第十六講：Occam‘s Razor)

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