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 |

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