Welcome to CSI5387, Data Mining and Machine Learning: Concepts, Techniques, and Applications
Offered in the Fall 2012 semester (Sep. –
Dec.
2012):
Fri 4-7PM, CBY B012
Class starts Sep. 7.
CBY building is in the southern part of the uOttawa campus.
Check the map at http://www.uottawa.ca/maps/
Instructor: Dr. Stan Matwin
www.eecs.uottawa.ca/~stan
Office hours: TBA, for now, just send me an email for a meeting: stan@eecs.uottawa.ca
This course is
changed from year to year, and the existing
slides may be modified.
Course syllabus
If you have not taken an AI
class:
I recommend you read the following chapters
from
"AI: the Modern Approach" by Russeell, Norvig, 3rd edition:
Assignments
Assignment 1 is here,
due Oct 8, and its dataset is here
Solution for Assignment 1 is here.
Assignment 1 marks are here.
Some marks are still TBD.
Project
Here is project statement
project presentation
SM
project presentation
Erico
Here are Cohen papers 1 and 2
Here is our paper.
dataset Estrogen
dataset Triptan
dataset Oral
Hypoglycemics
dataset Beta Blockers
Course marks
(including
project and final exam) will be here
Final exam:
Dec. 14,4-7PM, CBY B205
2010
final
exam
2009 final exam
Course marks
TBA
set 1
additional material for class 1 and 2 (by
V.
Kumar, University of Minnessota)
set 2
additional material on
instability of
DTs for class 3
additional slides on instability of
DTs for
class 3 (with thanks to Rob Holte and Ken Dwyer, U of A)
additional material on PAC for
class 3
set 3
set 4
set 5
set 6
set 7
set 8
cost curves slides by Dr.C. Drummond
set 9
Book[s]
I do not recommend a single textbook for this class because there isn’t one that will cover all our needs. I will use material from three books:
Mitchell,
T., “Machine Learning”. This is an excellent book, but at this
point
quite a bit outdated. Tom promises to finish a new edition, and
we are
ll waiting!!
Flach,
"The Art and Science of
Algorithms that Make Sense of Data", to appear, Cambridge University Press,
Sep.
2012. See here
for the first two chapters list of contents.
Hastie, T. Tibshirani, R. Friedman, J., "The Elements of Statistical Learning", an excellent textbook for part of the material, with a highly mathematical slant
Cornuéjols, A., Miclet, L.
“Apprentissage
artificiel: consepts et algorithmes" (don’t worry, you can make
it thru
this class without knowing French)
Han,
J.,
Kamber, M. "Data Mining. Concepts and techniques"
And no panic, you do NOT need to buy five or six books. I am working to have all of them on reserve in the uOttawa library. Some of these books are available s e-books, and only chapters will be recommended as additional reading The main material for the course will be my .ppt slides and the papers listed below.