Advanced Topics in Data Mining Techniques, Fall 2007


ST: CS504	Advanced Topics in Data Mining Techniques	Fall 2007
download a flyer for this course course info from UI class schedule How to get enrolled: INL employees , other students class period: Tuesday/Thursday 12:00pm - 1:15pm mdt office hours: Tuesday & Thursday, 11:00am - 12:00pm mdt (please make an appointment)
Offered: Idaho Falls (live - room PLEASE NOTE THE ROOM CHANGE - CHE219) Also as a web (online) course via Web CT (please contact me about details). This option is for students outside of Idaho Falls and for those that cannot attend live session every time.

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Excerpt from catalog data:
CS504 ST: Advanced Topics in Data Mining Techniques:
This is an advanced topics course in Data Mining or Knowledge Discovery from Data (KDD) Systems. The course will develop a theoretical framework for major issues in data mining. Data preprocessing, reduction, data warehouse to data mining, mining frequent patterns, associations, and correlations, classification and prediction, cluster analysis, and mining stream, time-series, and sequence data mining, Bayesian classification, decision tree induction, classification by backpropagation and support vector machines, will be emphasized through optimization techniques, aspects of fuzzy logic and neural networks.
Because of the multifaceted nature of the course, only undergraduate seniors with mathematical and system maturity will be permitted to take this graduate course. Mastery of the theoretical foundations will be tested but the major goal of this course is the design and implementation of a advanced data mining system with expectations of optimal performance and flexibility. Prereq: Graduate standing or instructor permission. This course is offered for the first time at the University of Idaho!

We will talk about applications ranging from intelligence gathering, to web, multimedia, marketing, and customer relationship data mining. This course has relevance to INL in particular but can fill a Special Topics need for the general student.

Class description (pdf )
Class policy (pdf )

Instructors:
Milos Manic, Ph.D.
University of Idaho

UIIF College of Engineering at IF, UIIF CS Dept
1776 Science Center Drive, TAB Ste.#303,
Idaho Falls, ID 83402;
ph. direct: 208.282.7845; fax:208.282.7950;

email: misko@uidaho.edu
url: http://www.cs.uidaho.edu/people.html/
url: http://husky.if.uidaho.edu

Important links

Here you can find links on
- UI courses, schedule, academic calendars, how to get enrolled, libraries, tutoring and academic assistance programs, disability support services, and many others.
Idaho Falls students:
- Register for the course at the ISU/UI website: http://my.isu.edu/direct (Index Number UCS 504, and choose either 292720 or 292750)
  - INL employees: Fill out the INL Form 360.03A in Lotus Notes
UI academic schedule and to get enrolled

Textbook:

Required material besides textbooks will be provided through web-based documentation..This course is a compilation of large number of books. It is very hard to choose a single textbook that would cover them all. I will go through each of these books in our first class.

Textbooks:
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Hardcover: 770 pages, Publisher: Elsevier Science Ltd (The Morgan Kaufmann Series in Data Management Systems); 2nd edition (Apr 2006), ISBN-10: 1558609016, ISBN-13: 9781558609013 links 1 2 Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Paperback: 525 pages, 2 edition (June 10, 2005), Publisher: Morgan Kaufmann (Morgan Kaufmann Series in Data Management Systems); ISBN-10: 0120884070, ISBN-13: 978-0120884070.Fuzzy Modeling and Genetic Algorithms For Data Mining and Exploration, Earl Cox, Dec 2004, Format: Paperback: 530 pages, Publisher: Elsevier Science Ltd (The Morgan Kaufmann Series in Data Management Systems), ISBN-10: 0121942759, ISBN-13: 9780121942755

Recommended books (related more towards applications):

Web Data Mining and Applications in Business Intelligence and Counter-Terrorism, Bhavani Thuraisingham, ISBN-10: 0849314607, ISBN-13: 9780849314605, Hardcover, June 2003, CRC Press.

Mining the Web: Discovering Knowledge from Hypertext Data, Soumen Chakrabarti, Hardcover, ISBN-10: 1558607544, ISBN-13: 9781558607545, 2002, Morgan Kaufmann Pub. Mining the Web: Transforming Customer Data into Customer Value, Author: Gordon Linoff, Michael J. A. Berry, Format: Paperback: 348 pages, Feb 2002, Publisher: John Wiley & Sons Inc, ISBN-10: 0471416096, ISBN-13: 9780471416098
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Author: Berry, Gordon S. Linoff, Format: Paperback, 648 pages, Edition: 2 ( Apr 2004), Publisher: John Wiley & Sons Inc, ISBN-10: 0471470643, ISBN-13: 9780471470649

In addition, an extensive reference material can be found at my list of references on data mining , neural networks , and intelligent control . This is also a good place to start when you start working on your project/research paper.

Software downloads

Course downloads site
Online references, tutorials, books, examples
FAQ's page

Course Material

Class notes (passw. req.)
Homework & exam assignments (passw. req.)
Homework should be sent to misko@uidaho.edu with the subject: "[CS504] HW1 Family_name" for homework number 1, "[CS504] HW2 Family_name" for homework number 2, etc. Please follow the instructions on deliverables and report form, specified in Class Policy and at the end of each homework. Attachments in doc, PDF, or ps format are welcome. Please compile your results into a single file only.

Questions:

FAQ's page
SUBJECT LINE: Please, use [CS504] Family_name Question. I might be receiving over 100 emails a day. If you do not want yours to get lost, please always double check your subject line. If urgent response is needed, please use [CS504] URGENT Family_name Question.
SIGNATURE: Please, sign your email with Family_name, First_name Middle_name, Stud_ID: xxx-xxxxx, course section (possible section are: CS504_XX). Some of the names are fairly similar, and this way we will avoid confusion and facilitate faster correspondence.

Grades

You can check your grades here (passw. req.)
IMPORTANT: To be assigned a password, send me an email to misko@uidaho.edu with following information: full name, student ID, course section (possible section is only: CS504_xx), email, and mailing address that you want to be used for sending graded assignments.
NOTE: Please be aware that the correct mailing address needs to be updated on Banner too! Also, please note that starting July 1, 2003, the only official email account is your VandalMail account. Please note that I will not be replying to any other account such as yahoo, hotmail, etc.

Course info from UI schedule page

Course information from Registrar's UI academic class schedule http://www.uidaho.edu/schedule/if/200710CS.htm:

Computer Science
SUBJ		CRSE	SEC	CREDITS		TITLE		CAMPUS
		START DATE		END DATE		TIMES	DAYS	BLDG
		INSTRUCTOR(S)				DELIVERY METHOD
193507	CS	504	40	3	ST:Advanced Topics in Data Mining Techniques		Idaho Falls (IFCHE)
		Aug 21, 2007		Dec 14, 2007	12:00 pm - 01:15 pm	TR	UPHEC	307
		Milos Manic

INL Employees - how to get enrolled

The INL intranet has an education programs section that gives a step by step process on what to do at http://educationprograms.inel.gov/index.aspx
- In short, steps are:
  1. Talk with your manager about it and get approval.
  2. Complete the student registration with the university.
  3. Complete form 360.03A in Lotus Notes and submit it for manager approval
  4. Upon manager and University approval the employee will receive a copy.

Other students - how to get enrolled

Go to the http://classes.isu.edu/
- then click on University of Idaho pull down menu and click on UCS. This class is UCS 504 Advanced Topics in Data Mining, registration number 19350