|
|
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
||||||||||
|
Early Morning |
COLT |
T1 |
W1 |
COLT |
ILP |
|
W2 |
W3 |
W4 |
COLT |
ILP |
ICML |
ILP |
ICML |
ICML |
|
Late Morning |
COLT |
T2 |
W1 |
COLT |
ILP |
|
W2 |
W3 |
W4 |
COLT |
ILP |
ICML |
ILP |
ICML |
ICML |
|
Early Afternoon |
COLT |
T2 |
W1 |
COLT |
ILP |
T3 |
W2 |
W3 |
W4 |
COLT |
ILP |
ICML |
ILP |
ICML |
ICML |
|
Late Afternoon |
COLT |
|
W1 |
COLT |
ILP |
|
W2 |
W3 |
W4 |
COLT |
ILP |
ICML |
ILP |
ICML |
ICML |
|
Evening |
Reception |
Reception |
Banquet |
Reception |
|
||||||||||
T 2: Inside WEKA --
and Beyond the Book
Ian
H. Witten, Eibe Frank, Bernhard Pfahringer, Mark Hall
T 1: Bayesian
Kernel Methods
Alexander
J. Smola
T3: Introduction
to Minimum Length Encoding Inference
Dr
David Dowe
WS1: Text Learning
Marko
Grobelnik, Natasa Milic-Frayling, Dunja Mladenic
WS2: Machine Learning in Computer
Vision
Arcot
Sowmya, Tatjana Zrimec
WS3: Data Mining Lessons Learned
Nada
Lavrac, Hiroshi Motoda, Tom Fawcett
WS 4: Development of Representations
Edwin
de Jong, Tim Oates