Differences

This shows you the differences between two versions of the page.

--- grades [2014/07/25 17:03] – nick
+++ grades [2014/08/22 15:11] (current) – nick
@@ Line 1: / Line 1: @@
-====== Course Description, Learning Objectives, Grading, Materials ======
+====== Grades ======
-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, useful and previously unknown
+Grades should/will follow the distribution:\\
-knowledge from large databases. It is a highly inter-disciplinary area representing the confluence of
+ 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 (expected learning outcomes):
-Learning objectives include:
+Graduate students are expected to complete all of 4412 and attend all classes.
-     *  Knowledge of the terminology and concepts of data mining;
-     *	Insight into the possibilities and fundamental limitations of dta mining;
-     *	Insight into the relative advantages and disadvantages of major approaches to data mining;
-     *	Understanding of the basic methods and techniques used in data mining;
-     *	Skills in applying the basic methods and techniques to actual problems in data mining.
-Classes Tues/Thurs 13:00-14:30 Lassonde 3033
-Office Hours Wednesdays 12:00 or by appointment
-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)
-Class Materials
-  *   1.	Many many class handouts.
-  *   2.	Copies of Data Mining books.
-  *   3.	Copies of many many relevant papers.
-  *   4.	Many other notes.
-References
-  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.
-    Some conference/journal papers (will be posted over the semester).
-Grads are expected to complete all of 4412 and attend all classes.