Syllabus: |
Fundamental issues in intelligent systems - history of artificial intelligence, fundamental definitions, modelling the world, the role of heuristics. Search and constraint satisfaction - problem spaces, search techniques, constraint satisfaction. Knowledge representation and reasoning - review of proposition and predicate logic, resolution and theorem proving, probabilistic reasoning and Bayes theorem. Advanced search - genetic algorithms, simulated annealing, local search. Machine learning and neural networks - definition and examples of machine learning, supervised learning, learning decision trees, learning neural networks, learning theory, the problem of overfitting and unsupervised learning. Robotics - overview, state of the art, planning versus reactivecontrol, uncertainty in control, sensing, world models, configuration space, planning, sensing, robot programming, navigation and control. Data mining - the usefulness of data mining, associative and sequential patterns, data clustering, market basket analysis, data cleaning, data visualisation. |