course_outline
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| course_outline [2014/08/12 16:55] – nick | course_outline [2014/08/12 17:05] – nick | ||
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| * Part VI: Student Presentations | * 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) | ||
| **Class Materials** | **Class Materials** | ||
| Line 46: | Line 42: | ||
| **References** | **References** | ||
| - | | + | Recommended Textbook |
| * Jiawei Han, Micheline Kamber and Jian Pei, Data Mining -- Concepts and Techniques. Morgan Kaufmann, Third Edition, 2011. | * 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.\\ | * 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 Ed.), Morgan Kaufmann, 2005.\\ | * Ian H. Witten and Eibe Frank, Data Mining -- Practical Machine Learning Tools and Techniques (2nd Ed.), Morgan Kaufmann, 2005.\\ | ||
course_outline.txt · Last modified: 2014/09/08 17:46 by nick