Preliminary knowledge
Homework Zero (Basic Knowledge of Probability Statistics, Linear Algebra, Differentiation)
reference books
Learning from Data: A Short Course , Abu-Mostafa, Magdon-Ismail, Lin, 2013.
classic literature
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)
YS Abu-Mostafa, X. Song , A. Nicholson, M. Magdon-ismail. The bin model, 1995. (Lecture 4: The origin of the bin model)
V. Vapnik. The nature of statistical learning theory, 2nd edition, 2000. (Lectures 5 to 8: Complete mathematical derivation and extension of VC dimension and VC bound)
YS Abu-Mostafa. The Vapnik-Chervonenkis dimension: information versus complexity in learning. Neural Computation, 1(3):312-317, 1989. (Seventh Lecture: The Concept and Importance of VC Dimension)
references
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)
YS Abu-Mostafa. Machines that think for themselves. Scientific American, 289(7):78-81, 2012. (Lecture 1: Application of ML in "Clothing")
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. (Lecture 1: Application of ML in “Residential”)
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. (Lecture 1: The Netflix Contest)
SI Gallant. Perceptron-based learning algorithms. IEEE Transactions on Neural Networks, 1(2):179-191, 1990. (Second lecture: Origin of pocket, note that the actual pocket algorithm is more complicated than what we introduce)
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. (Lecture 4: The official version of No free lunch)
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)