Schedule of topics

Below is a schedule of the topics of the course. Weeks start on Thursdays before the mid-term break; on Mondays thereafter.

Week Starting: Lecture: Examples Class: Lab:
Th Sept 23 (A) Introduction    
Th Sept 30 (B) Bayesian approaches    
  Bayes II Probability Review  
Th Oct 7 (A) Learning theory I    
  Learning theory II   Bayes lab 1
Th Oct 14 (B) Alternatives to prob.    
  Neural Network approaches Bayes  
Th Oct 21 (A) Perceptrons    
  Multi-layer perceptrons   Bayes lab 2
Th Oct 28 (B) Applications of MLPs    
  Applications II Neural Networks  
Mid-term Break
Mon Nov 8 (A) Decision Trees    
  ID3   Neural Networks lab 1
Mon Nov 15 (B) ID3    
  Search Techniques Decision Trees  
Mon Nov 22 (A) Search    
  Genetic Algorithms   Neural Networks Lab 2
Mon Nov 29 (B) Genetic Algorithms    
  Unsupervised Learning Genetic Algorithms  
Mon Dec 6 (A) Unsupervised Learning    
  Unsupervised Learning   Genetic Algorithms lab
Mon Dec 13 (B) Review I    
  Review II   Lab Catchup

General timetable information can be obtained by clicking here

Back to syllabus by clicking here

Jon Shapiro