CSI 7163B – COMP6605P
Advanced topic in Computer
Systems: Machine Learning and Data Mining Systems
Category: S, A
Tue, 1-4; r. room LPR154 for the
first class, later SITE5084
Instructor: Dr. Stan Matwin,
SITE 5100, stan@site.uottawa.ca
Website:
www.site.uottawa.ca/~stan/csi/7163
Assignment 1 is here
Project is here
Message from William 29/3 is here:
Dear Students,
I apologize for the short notice, but I
think I can manage an information
session to help you out with questions you
have regarding your projects.
I will make myself available from
the Tamale Lab room SITE 3-033. One note
though, I must leave for a
meeting after then, please keep that in
mind.
In addition, I will have your assignments
ready to hand back on Tuesday
after Tamale seminar. You can come to the
same room shown above to get
your assignments back (but only after
Tamale, since I have an engagement
before then).
Kind Regards
WE
Project help for data conversion to the
BOW and TF/IDF format is here
Project is due Apr. 3
Slides on the t-test are here
In this class, we will cover the
basics of Machine Learning and Data Mining (classifier induction; Decision
Trees, Support Vector Machines, Naïve Bayes; selected applications: text
mining, bioinformatics).
Hands on experience with WEKA – the open source, state of the art data
mining suite. The class is combined with a research seminar in data and text
mining.
There are no formal prerequisites
beyond general familiarity with Artificial Intelligence. Please consult the
instructor if in doubt.
The grades will consist of: a
hands-on data mining project in Weka, participation in the seminar part, and an
essay; exact breakdown to be given later.
Depending on the number of
students, a presentation may be added to this.
Textbook:
Data Mining : Practical Machine Learning
Tools and Techniques, Second Edition by Ian H. Witten and Eibe Frank (Paperback
- 2005)
Class notes (.ppt) will be published later
Slides used in class are here, .ppt and .pdf (2 per page)
WEKA workshop slides by William Elazmeh
are here
The paper on feature selection for text
classification is here
Introduction to SVM is here
The Scholkopf paper is here