Week Starting: | Lecture: | Examples Class: | Lab: |
Th Sept 23 (A) | Introduction | ||
Probability | |||
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 |