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--- course_outline [2007/07/31 19:53] – external edit 127.0.0.1
+++ course_outline [2014/09/08 17:46] (current) – nick
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 ====== Course Outline ======
+**wiki: https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/**\\
-The course outline is a guideline to topics that will be discussed in the course, and when they will be discussed:
+====== Course Description, Learning Objectives, Grading, Materials ======
-===== Week 1 =====
+**Course Description:**
+Data mining is computationally intelligent extraction of interesting, useful and previously unknown
+knowledge from large databases. It is a highly inter-disciplinary area representing the confluence of
+machine learning, statistics, database systems and high-performance computing. This course
+introduces the fundamental concepts of data mining. It provides an in-depth study on various data
+mining algorithms, models and applications. In particular, the course covers data pre-processing,
+association rule mining, sequential pattern mining, decision tree learning, decision rule learning,
+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):
-Your notes here.
+Learning objectives include:
+     *  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.
-===== Week 2 =====
+Classes Tues/Thurs 13:00-14:30 Lassonde 3033
-===== Midterm =====
+Office Hours Wednesdays 12:00 or by appointment
-===== Drop Deadline =====
+**Topics**
+     * Course Introduction
+     * Part l: Data Mining Algorithms, Techniques and WEKA
+     * Part ll: Useful Machine Learning Techniques 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
-===== Week 13 =====
-===== Final Exam =====
+**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 Ed.), 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).
+Graduate students are expected to complete all of 4412 and attend all classes.