Machine-Learning-Foundations-and-Techniques

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Machine-Learning-Foundations-and-Techniques

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Coursera 机器学习基石机器学习技法林轩田课堂PPT、作业及课堂笔记。

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# Machine Learning Foundations and Techniques

Coursera 机器学习基石 机器学习技法 林轩田 课堂PPT、作业及课堂笔记。

***由于笔记里存在公式编辑，请自行下载chrome的扩展程序[GitHub with MathJax](https://chrome.google.com/webstore/detail/github-with-mathjax/ioemnmodlmafdkllaclgeombjnmnbima)。***

## Professor

[Hsuan-Tien Lin](https://www.csie.ntu.edu.tw/~htlin/)

## Coursera

- [機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations](https://www.coursera.org/learn/ntumlone-mathematicalfoundations)
- [機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations](https://www.coursera.org/learn/ntumlone-algorithmicfoundations)

## bilibili

- [机器学习基石（林轩田）](https://www.bilibili.com/video/av1624332)
- [机器学习技法（林轩田）](https://www.bilibili.com/video/av4180636)
- [林轩田老師浅談机器学习、大数据、与人工智慧｜机器学习技法](https://www.bilibili.com/video/av6989521/?from=search&seid=7956500422865440847)

## 延伸阅读

### 預備知識

作業零 (機率統計、線性代數、微分之基本知識)。

### 參考書籍

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

### 經典文獻

- 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)

## 网络资源参考

1. [機器學習基石 机器学习基石（Machine Learning Foundations） 作业1 习题解答](http://blog.csdn.net/a1015553840/article/details/50986313)
1. [台大林轩田《机器学习基石》学习笔记：重要工具一（Feature transform）](http://blog.csdn.net/qq_22717679/article/details/51179198)
1. [台大林轩田机器学习课程笔记3----机器学习的可行性](http://blog.csdn.net/SteveYinger/article/details/51171828)
1. [台大林轩田·机器学习基石记要](http://blog.csdn.net/qiusuoxiaozi/article/details/51558497)
1. [林轩田--机器学习基石&机器学习技法](http://blog.csdn.net/youyuyixiu/article/details/54317895)
1. [台大机器学习技法学习笔记](http://blog.csdn.net/frankchen0130/article/details/50801852)
1. [机器学习技法--Blending and Bagging](https://www.jianshu.com/p/4dfd361e1db6)
1. [台大林轩田·机器学习技法记要](http://blog.csdn.net/qiusuoxiaozi/article/details/51759571)
1. [不可错过的MOOC：台大《机器学习技法》](http://www.iliuye.com/index.php/Wap/Index/article/id/102124)

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