Level | Credits | Class Time Distribution | Prerequisites |
---|---|---|---|
UG | 3 | 3 cl | 670; 680; or permission of instructor |
Number of Hours | Topic |
---|---|
3 | Introduction to KDD process and basic statistics |
3 | Data preprocessing |
6 | Classification algorithms |
6 | Clustering algorithms |
6 | Scalable data mining algorithms and systems support, parallel algorithms, database integration, data locality issues (embedded topic, frequent pattern algorithms) |
5 | Scalable and parallel algorithms |
5 | Applications |
2 | Reviews and exams |
Homeworks and Lab Assignments | 60% |
Midterm | 20% |
Final | 20% |
a | b | c | d | e | f | g | h | i | j | k |
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XXX | XXX | X | XX | XX | X | XX | X | X | X | XX |
1a | 1b | 1c | 2a | 2b | 2c | 3a | 3b | 4a | 4b | 5a | 5b | 5c |
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XX | XX | XX | XX | X | XX | X | XX | XX | XX | XX | X |