CSE454 plan 2003

17 Feb 2003, L.A.

CSE454 Learning and Prediction I: Data Mining (LA) semester 1 (all)

Working document, subject to change

Topics include: Elementary information theory (including noiseless coding and compression); introduction to inductive inference and prediction; data modelling and data mining; introduction to Minimum Message Length (MML) inference; clustering, mixture modelling and unsupervised classification; supervised classification and decision trees. Example applications may be described.

• 1: prob' (codes), info', terminology,
• 2: estimators, entropy, KL-dist, prob' pred'n
• 3: (Snob out of synch') derive binomial ?3-ways? + Fisher,
• 4: give multinomial maxL MML minEKL,
• 5: normal, give Fisher, maxL, MML
• 6: [Unsupervised] mixture modelling + Snob
• 7: other prob' distributions
• 8: [Supervised] decision trees 1
  [[unsupervised classification prac due]]
• 9: decision trees 2
• 10: dist'ns and codes for integers
• 11: KL defn on a mult, on a normal
• 12: codes++, Kraft, Huffman, Shannon-Fano; application advert' (for MML:-)
  [[supervised classification prac due]]

. . . CSE455 Learning and Prediction II: MML Data Mining (dld) semester 2