Fundamentals of Data Mining
Fall 2014
CPTS 483 & 580, Tuesday and Thursday, Noon-1:15pm
Instructor: John Miller PhD
Office: West 134E WSU Tri-Cities
jhmiller@tricity.wsu.edu
Class web page can be found at
http://www.tricity.wsu.edu/~jhmiller
Required Textbook: Learning from Data
By: Abu-Mostafa, Magdom-Ismail and Lin
Website: AMLbook.com
Contains slides and video of course at Caltech
Short text (200 pages)
Central theme: Can data-mining results be trusted?
Caltech course goes beyond text
Probably also true for this class
Grades:
Assignments, quizzes, and final exam have equal weight of 1/3
Graduate credit requires a project approved by instructor
Tests given in class with open books and lecture notes
Quizzes designed to reward reading text and lecture notes
Final exam will contain problems like those worked in class
Assignments will require programing
See me if your can’t make their due date
Graduate project reports 5-10 pages double spaced with figures
Due last class period before exam week
Objectives of the class:
1. Lean basic methods of data mining
2. Lean basic principles that ensure quality
More nuts and bolts
Accommodations for Disabled Students:
Reasonable accommodations are available for students who have a
documented disability. If you have a documented disability, even
temporary, make an appointment as soon as possible with the Disability
Services Coordinator,
Cherish Tijerina, 372-7352, ctijerina@tricity.wsu.edu
You will need to provide your instructor with the appropriate classroom
accommodation form. The forms should be completed and submitted
during the first week of class. Late notification may delay your
accommodations. All accommodations for disabilities must be approved
through Disability Services. Classroom accommodation forms are
available through the Disability Services Office.
More nuts and bolts
Academic Integrity:
As stated in the WSU Tri-Cities Student Handbook," any member of the
University community who witnesses an apparent act of academic
dishonesty shall report the act either to the instructor responsible for the
course or activity or to the Office of Student Affairs."
The Handbook defines academic dishonesty to include "cheating,
falsification, fabrication, multiple submission [e.g., submitting the same or
slightly revised paper or oral report to different courses as a new piece of
work], plagiarism, abuse of academic material. complicity, or misconduct in
research."
Infractions will be addressed according to procedures specified in the
Handbook.
More nuts and bolts
Safety:
Should there be a need to evacuate the building (e.g., fire alarm or some
other critical event), students should meet the instructor at the Cougar
statue directly outside of the West building. A more comprehensive
explanation of the campus safety plan is available at
http://www.tricity.wsu.edu/safetyplan/
The university emergency management plan is available at
http://oem.wsu.edu/emergencies/
Further, an alert system is available. You can sign up for emergency
alerts (see http://alert.wsu.edu) through the zzusis site
(http://portal.wsu.edu/).
Student Concerns.
If you have any student concerns, you can contact Carol Wilkerson
the Director of Student Affairs in West 269F, (509) 372-7139,
or carol.wilkerson@tricity.wsu.edu.
If you have any concerns about this class, you should contact your
instructor first, if possible.
Attendance Policy.
Absences should be avoided. Students should contact an instructor if an
absence from class is unavoidable.
Students are encouraged to read Section 73 (Absences) of the
Washington State University Academic Regulations, which is found in the
WSU Tri-Cities Student Handbook.
Tentative Schedule
Tu Aug 19
Th Aug 21
Tu Aug 26
Th Aug 28
Tu Sep 2
Th Sep 4
Tu Sep 9
Th Sep 11
Tu Sep 16
Th Sep 18
Tu Sep 23
Th Sep 25
Tu Sep 30
Th Oct 2
Tu Oct 7
Th Oct 9
Tu Oct 14
Th Oct 16
Tu Oct 21
Th Oct 23
Tu Oct 28
Th Oct 30
Tu Nov 4
Th Nov 6
Tu Nov 11
Th Nov 13
Tu Nov 18
Th Nov 20
Tu Nov 25
Th Nov 27
Dec 2,4
Dec 8-12
Discussion of class syllabus
Introduction to supervised machine learning
Introduction to supervised machine learning
Introduction to Bayesian statistics
Introduction to Bayesian statistics
Parametric methods
Parametric methods
Multivariate Data
Multivariate Data
Test #1
Artificial Neural Networks
Artificial Neural Networks
Artificial Neural Networks
Artificial Neural Networks
Artificial Neural Networks
Genetic Algorithm
Genetic Algorithm
Radial basis functions
Radial basis functions
Test #2
Self-organizing maps
Self-organizing maps.
Advanced applications
Advanced applications
Advanced applications
Support Vector machines
Support Vector machines
Support Vector machines
Thanksgiving break
Thanksgiving break
Review
Finals week Test #3