Course Introduction

《Machine Learning》
Prerequisite：Mathematical analysis Linear algebra Probability Recommended for： graduate students Introduction： We introduce the concept of machine learning and several useful learning methods including linear models, nonlinear models, margin-based approaches, structured models, dimension reduction, unsupervised learning (Clustering), ensemble classifiers. Also some special topics and applications will be discussed. Syllabus： Introduction to Machine Learning Fundamental of Mathematics Background Three Fundamental Learning Algorithms k-Nearest Neighbor Algorithm Naive Bayes Algorithm The Perceptron Algorithm Evaluating what’s been Learned Confusion Matrix: False Positive, False Negative Receiver Operating Characteristic (roc) Curve k-fold Cross Validation Pairwise t-test Support Vector Machines Generalization Theory Bias vs. Variance VC-dimension Ensemble Learning Adaboosting Online Learning Unsupervised Learning k-means Algorithm Mixture of Gaussians EM algorithm Dimension Reduction Large Scale Machine Learning Reference： Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin (2012). Learning From Data. AMLbook.com Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111 Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8. Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2. Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3. Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7. MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1. Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7. Ian H. Witten and Eibe Frank Data Mining: Practical machine learning tools and techniques Morgan Kaufmann ISBN 0-12-088407-0. Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0387952845. Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031.

《Machine Learning》

Prerequisite：Mathematical analysis Linear algebra Probability
Recommended for： graduate students
Introduction：

We introduce the concept of machine learning and several useful learning methods including linear models, nonlinear models, margin-based approaches, structured models, dimension reduction, unsupervised learning (Clustering), ensemble classifiers. Also some special topics and applications will be discussed.

Syllabus：

Introduction to Machine Learning
Fundamental of Mathematics Background
Three Fundamental Learning Algorithms
k-Nearest Neighbor Algorithm
Naive Bayes Algorithm
The Perceptron Algorithm
Evaluating what’s been Learned
Confusion Matrix: False Positive, False Negative
Receiver Operating Characteristic (roc) Curve
k-fold Cross Validation
Pairwise t-test
Support Vector Machines
Generalization Theory
Bias vs. Variance
VC-dimension
Ensemble Learning
Adaboosting
Online Learning
Unsupervised Learning
k-means Algorithm
Mixture of Gaussians
EM algorithm
Dimension Reduction
Large Scale Machine Learning

Reference：

Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin (2012). Learning From Data. AMLbook.com
Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8.
Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7.
MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1.
Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.
Ian H. Witten and Eibe Frank Data Mining: Practical machine learning tools and techniques Morgan Kaufmann ISBN 0-12-088407-0.
Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0387952845.
Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031.

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