<1998< ^CSE2304^ >progress 1999>

CSE423 Plan 1999

NO OFFICIAL STATUS, PLANNING ONLY

DLD's plan B (14/7/1999) and B' (16/7/1999):

The numbering does not necessarily reflect the lecture number.

  1. probability, discrete, continuous, cumulative, joint, independent, dependent, marginal, 1_lecture
  2. binomial distribution
  3. multinomial distribution
  4. Normal (Gaussian) distribution 2nd_lecture
  5. information, entropy
  6. Kullback-Leibler distance, footy tipping
  7. (prefix-) codes, examples, Huffman, arithmetic coding 4th_lecture
  8. integers (codes for), geometric, log*, tree-code, other (Poisson) ...
  9. inference, model class ... model ... parameter(s), estimation, bias, invariance
  10. Bayes, (max-) likelihood, prior, posterior,
  11. Fisher information matrix, general form of MML
    lect_10 and 11 loose on binomial distribution, multinomial distribution, Normal (Gaussian) distribution
  12. unsupervised classification, Snob. Use multinomial for class assignments, total assignment (and inconsistency), partial assignment and coding trick
  13. supervised classification, (decision-) trees and graphs, encode binomial/multinomial tree structure, encode continuous-valued cut points in tree structure, encode attribute being split upon, encode leaf nodes (1_1/2_lectures), decision graphs (1/2_lecture), applications - bushfire, proteins (1/2_lecture)
  14. sequential data, low-order Markov models, Lempel-Ziv, PFSA etc.
  15. applications: segmentation, ...
  16. (piece-wise) straight line fitting, linear regression, polygon fitting
  17. sequences and approximate matching
    May or may not get to:
  18. ?image compression, Peter T?
  19. ?causal models, Kevin K?

LA's plan A (6/7/1999):

The numbering is Lloyd's cut #1; it does not reflect lecture number, nor duration, nor ordering, yet. Looks like more than I had anticipated. Over to dld for plan-B

  1. probability, discrete, continuous, cumulative, joint, independent, dependent, marginal, Bayes, (max-) likelihood, prior, posterior, Kullback-Leibler distance
  2. inference, model class ... model ... parameter(s), estimation, bias, invariance
  3. information, entropy
  4. (prefix-) codes, examples, Huffman, arithmetic coding
  5. binomial distribution
  6. multinomial distribution
  7. integer (codes for), geometric, log*, tree-code, other...
  8. normal distribution
  9. sequential data, low-order Markov models, Lempel-Ziv, PFSA etc.
  10. Fisher information matrix, general form of MML
  11. applications: segmentation, ...
  12. (piece-wise) straight line fitting, linear regression, polygon fitting
  13. sequences and approximate matching
  14. unsupervised classification, Snob
  15. supervised classification (decision-) trees and graphs
  16. ?image compression, Peter T?
  17. ?causal models, Kevin K?


D. Dowe & L. Allison © 1999, Comp Sci and SWE, Monash University