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]
Why Can Machines Learn? [为什么机器可以学习]
How Can Machines Learn? [机器可以怎么样学习]
How Can Machines Learn Better? [機器可以怎麼樣學得更好]
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)
关于Machine Learning更多讨论与交流，敬请关注本博客和新浪微博songzi_tea.
NTU-Coursera机器学习:机器学习基石 (Machine Learning Foundations)
原文地址：http://blog.csdn.net/songzitea/article/details/43922131
