Machine Learning: Foundations (2001/2)

Available Data Sets:
  1. Iris: DataInformation.
  2. australian: DataInformation
  3. heart: DataInformation
  4. diabetes: DataInformation
More datasets: UCI Machine Learning database repository

Final Project:
assignment
branching program paper
Data sets: Download Delve classification datasets
 

Homeworks:
homework 1
homework 2
homework 3
homework 4
 
 

Classes Schedule(Tentative):

  1. Introduction  (slides,scribe).
  2. Bayesian Inference (slides, scribe)
  3. PAC model and Occam Razor (slides, scribe)
  4. Boosting and Experts (slides,scribe)
  5. Decision Lists and Decision Trees (Splitting Criteria) (slides,scribe)
  6. VC dimension I  - definition and impossibility result (slides,scribe)
  7. VC dimension II - sample bound ( slides, scribe )
  8. Artificial Neural Networks ( slides, scribe )
  9. Model Selection (slides, scribe )
  10. Decision tree  - Pruning  (slides, scribe )
  11. Support Vector Machine: SVM  (slides, scribe part I scribe part II )