9/12: Introduction (Download PDF)
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Reading material:
Z.-H. Zhou. Three perspectives of data mining. Artificial Intelligence, 2003, 143(1): 139-146.
H.-P. Kriegel, et al. Future trends in data mining. Data Mining and Knowledge Discovery, 2007, 15(1): 87-97.
Q. Yang and X. Wu. 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making, 2006, 5(4): 597-604.
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9/19: Data, Measurements, and Visualization (Download PDF)
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Reading material:
M. C. F. de Oliveira and H. Levkowitz. From visual data exploration to visual data mining: A survey. IEEE TVCG, 2003, 9(3): 378-394.
H. Liu, F. Hussain, C. L. Tan, and M. Dash. Discretization: An enabling technique. DMKD, 2002, 6(4): 393-423.
J. Dougherty, R. Kohavi, M. Sahami. Supervised and unsupervised discretization of continuous features. In Proceedings of ICML'95, 194-202, Tahoe City, CA.
X. Zhu and X. Wu. Class noise vs. attribute noise: A qualitative study of their impacts. AI Review, 2004, 22(3-4): 177-210.
Link: A javascript for simple data visualization
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9/26: Supervised Learning (Download PDF)
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Reading material:
Chapter 2 of Introduction to Machine Learning (E. Alpaydin, MIT Press, 2010).
L. Valiant. A theory of the learnable. Communication of the ACM, 27(11):1134-1142, 1984.
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10/10: Decision Tree and Neural Networks (Download PDF)
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Reading material:
Chapters 9 and 11 of Introduction to Machine Learning (E. Alpaydin, MIT Press, 2010).
R. Quinlan. Induction of decision trees. MLJ, 1:81-106, 1986.
A. Roy. Artificial neural networks - A science in trouble. SIGKDD Explorations, 2000, 1(2): 33-38.
G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504-507, 2006.
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10/17: Linear Models and Kernel Trick (Download PDF)
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Reading material:
Chapters 3, 4, 6, and 7 of Pattern Recognition and Machine Learning (C. M. Bishop, Springer, 2007) (You may find the ebook to download using Baidu.com)
C. J. C. Burges. A tutorial on support vector machines for pattern recognition. DMKD, 1998, 2(2): 121-167.
K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE TNN, 2001, 12(2): 181-201.
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10/24: Bayesian Methods and Lazy Methods (Download PDF)
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Reading material:
D. Heckerman. Bayesian networks for data mining. DMKD, 1997, 1(1): 79-119.
H. Zhang. The Optimality of Naive Bayes. FLAIRS Conference 2004.
F. Zheng and G. I. Webb. A Comparative Study of Semi-naive Bayes Methods in Classification Learning. In AusDM'05, 141-156.
A. Andoni and P. Indyk. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. CACM, 2008, 51(1): 117-121.
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10/31: Discussion of Assignment 1 and Assignment 2
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11/7: Ensemble Methods (Download PDF)
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Reading material:
L. Breiman. Random Forest. Machine Learning 45 (1): 5–32.
Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (Chapter 2: Boosting).
E. Bauer and R. Kohavi. An Empirical Comparison of Voting Classication Algorithms: Bagging, Boosting, and Variants. Machine Learning, 1999, 36(1):105-139.
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11/14: Unsupervised learning: Density estimation and clustering (Download PDF)
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(The following arrangement is tentative) |
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11/21: Handling Big Data
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11/28: Experiment Design and Analysis / Discussion of Assignment 3
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12/5: Feature Extraction
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12/12: Score Functions and Optimization
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12/19: Applications: Content-based Information Retrieval
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12/26: Discussion of Assignment 4
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1/2: Q & A
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