Course structure
 •There will be 4 homework exercises
 •They will be theoretical as well as programming
 •All programming will be done in Matlab
 •Course info accessed from 
   www.cs.tau.ac.il/~nin
 •Final has not been decided yet
 •Office hours Wednesday 4-5 (Contact via email)
                           
                 Class Notes
 •Groups of 2-3 students will be responsible
   for a scribing class notes
 •Submission of class notes by next Monday 
   (1 week) and then corrections and 
   additions from Thursday to the following 
   Monday
 •30% contribution to the grade
                           
          Class Notes (cont’d)
 •Notes will be done in LaTeX to be 
   compiled into PDF via miktex.  
   (Download from School site)
 •Style file to be found on course web site
 •Figures in GIF
                           
   Basic Machine Learning idea
 • Receive a collection of observations associated 
   with some action label
 • Perform some kind of “Machine Learning” 
   to be able to:
    – Receive a new observation
    – “Process” it and generate an action label that 
     is based on previous observations 
 •Main Requirement:  Good generalization 
                            
          Learning Approaches
 •Store observations in memory and retrieve
    – Simple, little generalization (Distance measure?)
 •Learn a set of rules and apply to new data
    – Sometimes difficult to find a good model
    – Good generalization
 •Estimate a “flexible model” from the data
    – Generalization issues, data size issues