Topic List and Schedule

2/19		Course Overview & Introduction
2/26		Concept Learning (ML Ch. 2)	HW#1 Paper Reading
3/4		Data Preparation and Data Reduction (DM Ch. 2~3) Introduction to PCA and LDA
3/11		Data Preparation and Data Reduction (DM Ch. 2~3) Introduction to PCA and LDA
3/18		Break  (ICDAT 2004)
3/25		Decision Trees and Decision Rules (ML Ch. 3, DM Ch. 7)	HW#2 (Due 4/9) Feature Transformation
4/1		Bayesian Learning (ML Ch. 6)
4/8		Genetic Algorithms (ML Ch. 9, DM Ch. 10), presented by 陳善泰
4/15		Midterm
4/22		Bayesian Belief Networks (ML Ch. 6) Cluster Analysis (DM Ch. 6)	HW#3 Document Clustering (Due 5/31)
4/29		Cluster Analysis (DM Ch. 6)	HW#4 Document Title Generation (Due 6/20)
5/6		Association Rules (DM Ch. 8) (preliminary)
5/13		Artificial Neural Networks (ML Ch. 4, DM Ch. 9), presented by 黃耀民、張書維
5/20		Break (ICASSP 2004)
5/27		Paper Survey by  吳佳厚   Machine Learning for Information Extraction from XML, Semantic Web Workshop 2001 Paper Survey by  胡淑瓊   Genetic Algorithm-based Clustering Techniques
6/3		Paper Survey by  黃立德    Document Clustering with Committees, SIGIR 2002
6/10		An introduction to Support Vector Machine, presented by 黃立德 Hierarchical Document Clustering Using Frequent Itemsets, presented by 黃耀民
6/17
6/24		Final Exam

Textbook: 

1.		Mehmed M. Kantard, Data Mining: Concepts, Models, Methods and Algorithms, Wiley-IEEE Press, 2002.
2.		Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
3.		T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference, and Prediction, Springer-Verlag, 2001.

References:
  Books:

1.		Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
2.		Nello Christanini, John Shawe-Tayer, An Introduction to Support Vector Machines, Cambridge University Press 2000
3.		Michael Berthold and David J. Hand. Intelligent Data Analysis: An Introduction. Springer-Verlag, 2003.
4.		Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley Longman, 1999.
5.		I. H. Witten and E. Frank, Data Mining, Morgan Kaufmann, 2000.
6.		Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003.
7.		Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998.

Papers:

1.		" Machine Learning and Data Mining," T. Mitchell, Communications of the ACM, Vol. 42, No. 11, November 1999.
2.		"A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models," Jeff A. Bilmes, U.C. Berkeley TR-97-021