This course will be given in period 3 2002. Start Jan 25, 2002.
Fridays 13:15-15 in room 1537.
NEW BOOK: Today (dec 3 2001) I received a copy of
"Principles of Data Mining", MIT Press 2001, by
David J. Hand, Heikki Mannila and Padhriac Smyth.
It will be used as course book, and the supplementary
material will be correspondingly decreased.
The first part of the course material can be obtained from
Reading Assignments & Lecture plan
Send me an email if you want to participate!
Tentative reading list:
A: General Methodology :
- Keichii Noe: Philosophical aspects of Discovery Science
B: Bayesian methods:
- E. T. Jaynes: Probability theory: The logic of Science, Ch
- S.Arnborg: A survey of Bayesian Data Mining - Part I.
C: Markov Chain Monte Carlo
- Niclas Bergman: Recursive Bayesian Estimation, PhD Thesis, Linköping University.
D:Time series and prediction :
- Ch 1 of: Time Series Prediction: Forecasting the Future and
Past. Weigend, A. S., and N. A. Gershenfeld (Eds.) (1994) Santa Fe
Institute Studies in the Sciences of Complexity XV. (Proceedings of the
NATO Advanced Research Workshop on Comparative Time Series Analysis,
Santa Fe, NM, May 1992.) Reading, MA: Addison-Wesley.
E: Stochastic Complexity and Classification (unsupervised and
- (J. J. Oliver and D. J. Hand, Introduction to Minimum Encoding
Inference, [TR 4-94] Dept. Stats. Open Univ. and also
TR 94/205 Dept. Comp. Sci. Monash Univ. )
- G.I. Webb: Further Experimental Evidence against the Utility of
Journal of AI research 4(1996) 397-417.
- (J. J. Oliver and R. A. Baxter, MML and Bayesianism: Similarities and Differences, [TR 94/206] )
- Glymour, Cooper: Computation, Causation and Discovery(1999), Ch 2-3.
- ( N. Friedman, K. Murphy, S. Russel: Learning the structure of
probabilistic networks, Uncertainty in Artificial Intelligence, 1998.)
G: Support vector technology, applications in genomics.
- "Theory of SV Machines", CSD-TR-96-17, Royal Holloway, University of London, Egham, UK, 1996.
H: Visualisation of non-geometrical data..
(Buja et al (1996). Interactive
High-Dimensional Data Visualization. Journal of Computation and Graphical
Statististics. Vol 5, No. 1.)
Jan 25: Course overview, planning discussion
Hand, Mannila,Smyth: Ch1-3;
Arnborg: Survey of Bayesian Data Mining
Feb 1 12:15-13:45 : Inference
Feb 13, 9:15-11:00:
Hand, Mannila,Smyth: Ch4, 8, 9;
Cheeseman, Stutz: Autoclass.
Do you have data for graphical modeling?
Then take a look at B-course below.
Feb 15: No Lecture (advising individually)
Feb 22:Graphical and other models
Bergmans thesis, Ch3, Ch6.
Weigend and Gershenfeld: Time series prediction
HMS Ch 4, 5.
March 1 :Time series, dynamic models and estimation/prediction.
March 8: Estimation and particle filtering
March 15:EM, Autoclass classification
March 22: Support vector Techniques.
The Nada /misc directory has afs address /afs/nada.kth.se/misc