Introduction to Artificial Inteligence - Spring 2001
BGU Computer Science Department
Learning
- General models for learning
- Structure of a learning agent
- Performance element components
- Types of learning: supervised/unsupervised, reinforcement
- Inductive learning
- Learning decision trees
- Decision trees as classifiers
- Expressiveness and efficiency of decision trees
- Learning decision trees
- Performance evaluation of decision tree learning algorithms
- Applications
- Information theory in learning
- Information and information gain
- Noise and overfitting
- Extensions of the decision-tree framework: missing data, multivalued
and continuous attributes, predictive trees
- Artificial neural networks
- Natural neural networks - the brain
- Artificial neural networks
- Unit (computing element) types
- Network structures
- Network structure optimization
- Perceptrons
- Definition and representation strength
- Learning for perceptrons
- General feed-forward networks
- Training ANN using genetic algorithms
- Back-propagation algorithm
- Evaluation of the neural networks paradigm
- Applications: pronounciation, OCR, ALVINN