|Module Title||INTELLIGENT LEARNING|
|Co-ordinator||Dr Yonghuai Liu|
|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|
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