====== Course Outline ======
**wiki: https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/**\\

====== Course Description, Learning Objectives, Grading, Materials ======

**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):

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.

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 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


**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.