grades
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- | Course Description: | + | * Grading – Undergraduates (CSE 4412):\\ |
+ | * The course will be graded on the basis of one minor and substantial assignment (10%\\ | ||
+ | * and 15%), one in-class presentation (10%), one final exam (25%) and one project (40%).\\ | ||
+ | * \\ | ||
+ | * Grading – Graduate Students (CSE 6412):\\ | ||
+ | * The course will be graded on the basis of one minor and substantial assignment (10% \\ | ||
+ | * and 15%), one in-class presentation (10%), one final exam (10%), one paper(15%) and \\ | ||
+ | * one project (40%). \\ | ||
- | Data mining is computationally intelligent extraction of interesting, | + | Grades should/will follow |
- | knowledge from large databases. It is a highly inter-disciplinary area representing | + | A (90-100);\\ |
- | machine learning, statistics, database systems and high-performance computing. This course | + | B (80-89);\\ |
- | introduces the fundamental concepts of data mining. It provides an in-depth study on various data | + | C (70-79);\\ |
- | mining algorithms, models and applications. In particular, the course covers data pre-processing, | + | D (60-69);\\ |
- | association rule mining, sequential pattern mining, decision tree learning, decision rule learning, | + | uh oh (below 60) |
- | neural networks, clustering and their applications. The students are required to doprogramming | + | |
- | assignments to gain hands-on experience with data mining. | + | |
- | Objectives | + | |
- | Learning objectives include: | + | Graduate students |
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- | Classes Tues/Thurs 13:00-14:30 Lassonde 3033 | + | |
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- | Office Hours Wednesdays 12:00 or by appointment | + | |
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- | Topics | + | |
- | + | ||
- | * Course Introduction | + | |
- | * Part l: Data Mining Algorithms, Techniques and WEKA | + | |
- | * Part ll: Useful Machine Learning Tehniques for Data Mining | + | |
- | * Part lll: Statistical methods: Probabilistic Inference | + | |
- | * Part IV: Applications and Data Mining Systems | + | |
- | * Part V: Course review – one day | + | |
- | * Part VI: Student Presentations | + | |
- | + | ||
- | Grading | + | |
- | + | ||
- | The course will be graded on the basis of one minor and substantial assignment (10% and 25%), one major in-class presentation and one minor (15 min) project report (15%), and one project (50%). \\ | + | |
- | \\ | + | |
- | Grades should follow the distribution A (90-100); B (80-89); C (70-79); D (60-60); uh oh (below 60) | + | |
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- | Class Materials | + | |
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- | * | + | |
- | * | + | |
- | * | + | |
- | * | + | |
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- | References | + | |
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- | Recommended Textbook | + | |
- | * Jiawei Han, Micheline Kamber and Jian Pei, Data Mining -- Concepts and Techniques. Morgan Kaufmann, Third Edition, 2011. | + | |
- | + | ||
- | Reference Books | + | |
- | * Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006. | + | |
- | * Ian H. Witten and Eibe Frank, Data Mining -- Practical Machine Learning Tools and Techniques (2nd Edition), Morgan Kaufmann, 2005. | + | |
- | * S.M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998. | + | |
- | * Margaret H. Dunham, Data Mining -- Introductory and Advanced Topics, Prentice Hall, 2003. | + | |
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- | Some conference/ | + | |
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- | Grads are expected to complete all of 4412 and attend all classes. | + |
grades.txt · Last modified: 2014/08/22 15:11 by nick