Course Software and data

Library The Nada /misc directory has afs address /afs/nada.kth.se/misc

Some projects (more to come, I hope)

Clustering image pixels

Papers distributed :

Aug 22: A2,A3,B2-B5(Ch 1-4), C1-C5, D2-6, G1-G2
Sept 12: E3, E2, G3, G4
Sept 19: D7-D9.
Sept 26: F1-F3

Reading until Sept 5:

A2, A3, B2, B3 Fayyad Ch 1,2,(3),4

Reading until Sept 12:

B5, Ch 1-3; C1-C5; D2; E3 . In Fayyad: Ch 6, 7, 11, 12, 13, 19.

Reading until Sept 19:

D1-D6,

Reading until Sept 26:

E1-3 (again) ,

Reading until Oct 3:

C3, C5, F1-F3

Homework:

Find interesting KDD resource pointers and mail to me. If you have time: Make your own KDD home page!

Make a proposal for your own examination(evaluation). One or more of:

Project: discuss project goals and plans with me(SA), staff the advisory group and prepare a (short or long) class presentation. Projects finishing after course ends should be presented and communicated to class participants by some WWW method.

Paper: Analyze a general problem area, and make your own review and conclusions. Preferably presented in class. Possible topics:
Do Bayesians really own the truth?
True and false knowledge in data mining.
Relations between user and miner
hundreds more!!!

Presentation: Choose an interesting paper or set of papers within the area of KDD-DM. Give a mini-seminar in class, consisting of a summary followed by analysis and discussion.

IT IS TIME to start thinking about examination ...... The available slots must fill up before they occur in real time. The examination is built on the idea of active and mutual learning.

Scheduled activities:

  1. Aug 22: Presentation of participants and course content, detailed scheduling.
  2. Sept 5: 13:15:The Bayesian method in knowledge discovery and fitting ( Ch 3, 4, B 2-3) (room 1537, PDC)
  3. Sept 12, 10:15: Presentations of papers on Bayesian approach to model fitting (Sivia Ch 6) , Rough sets and clustering (discussion), groupwork on examination projects and problems (room 4618, CID)
  4. Sept 12, 13:15: "Learning to Recognize Volcanoes on Venus" Lars Asker will talk about the development of JARtool, an image database exploration tool. (room 1537, PDC)
  5. Sept 19, 13:15: On Bayesian knowledge discovery and stochastic complexity with an application to clustering of binary vectors. Timo Koski, Mathematical statistics, KTH, will talk about experience from a project on classification of bacteria. (room 1537, PDC)
  6. Sept 26, 10:15: Lars Arvestad, Nada: HMM in biochemistry
    11:15:Jakob Eriksson, D2: Grammar Extractor project
    Mats Andersson: Method presentation (room E36 Nada plan3)
  7. Sept 26, 13:15: Anders Holst, Thesis defence (Bayesian networks and neurocomputing) Kollegiesalen (not formally part of course)
  8. Oct 3, 10:15-12:00: Daniel Fagerström, CVAP: Time series anaysis: (E51 OB 14)
  9. ]
  10. Oct 3, 13:15:Anna Bergman och Ola Ahlqvist: (room 1537, PDC) Rough sets and attribute oriented induction: To reduce the number of tuples/rows in a set of task-relevant data stored in a relation table to mining knowledge rules for characteristic, discriminant, association, cluster rules etc. (room 1537, PDC)
  11. Oct 10, 13:15 Asa Rudström, DSV: (1537, OB 2)
  12. Dec 5, 13:15:in 1537 (PDC, Osquars Backe 2): Video show with informal project reportings:
    Xgobi: Dynamic Graphics for Data Analysis
    Grand Tour and Projection Pursuit
    Exploring Time Series Using Interactive Graphics
    Spatial CDF Estimation & Visualization with Applications to Forest Health Monitoring
    Dynamic Graphics in a GIS: Analyzing and Exploring Multivariate Data
    Missing Data in Interactive High-Dimensional Visualization

stefan@nada.kth.se