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
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(The following arrangement is tentative) |
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10/17: Linear Models
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10/24: Discussion of Assignment 1
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10/31: Parametric Methods, Lazy Methods, and Hasing
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11/7: Ensemble Methods
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11/14: Clustering
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11/21: Handling Big Data
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11/28: Experiment Design and Analysis / Discussion of Assignment 2
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12/5: Feature Extraction
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12/12: Score Functions and Optimization / Discussion of Assigment 3
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12/19: 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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