Module Identifier | CS36110 | |||||||||||
Module Title | INTELLIGENT LEARNING | |||||||||||
Academic Year | 2007/2008 | |||||||||||
Co-ordinator | Dr Yonghuai Liu | |||||||||||
Semester | Semester 1 | |||||||||||
Other staff | Professor Ross D King, Dr Yonghuai Liu, Dr Maria Liakata, Professor Mark H Lee, Dr Larissa Soldatova, Dr Simon M Garrett, Mr David J Smith | |||||||||||
Pre-Requisite | CS26110 or equivalent. | |||||||||||
Course delivery | Lecture | 20 lectures | ||||||||||
Assessment |
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Further details | http://www.aber.ac.uk/compsci/ModuleInfo/CS36110 |
Possibility and necessity of learning, target function, components in a learning system, performance measurement of learning systems.
2. Concept learning - 3 lectures
This chapter will use concept learning to develop applications to predict whether students like this module or not: Generality ordering of hypotheses, FIND-S algorithm, version space, the LIST-THEN-ELIMINATE algorithm, inductive bias
3. Decision tree learning - 3 lectures
This chapter will use decision tree learning to develop applications about banking business: Entropy, best attribute, information gain, best tree, inductive bias, Occam's razor, over-fitting, reduced error pruning, rule post-pruning.
4. Artificial neural network - 3 lectures
This chapter will use artificial neural network learning to develop applications to recognize handwritten characters: Perceptron, linear separability, gradient decent, sigmoid function, back propagation algorithm, over-fitting.
5. Bayesian learning - 4 lectures
This chapter will use Bayesian learning to develop applications about medical diagnosis: Bayesian theory, maximum a posteriori hypothesis, maximum likelihood, probability density, normal distribution, minimum description length principle, Bayes optimal classifier, naive Bayes classifier.
6. Genetic algorithm - 3 lectures
This chapter will use genetic algorithms to develop applications about artificial life: Hypothesis representation, best hypothesis, genetic operators, fitness function, fitness proportionate selection, flocking behaviour, local control.
7. Reinforcement learning - 2 lectures
This chapter will use reinforcement learning to develop applications about robot path optimization: Reward, Markov decision process, utility function, Q-learning.
This module is at CQFW Level 6