Problem solving and search:
review of problem solving techniques, advanced heuristic search techniques, space efficient techniques, real-time search.
robot planning, task planning and scheduling, means-ends planning, partial order planning, planning graphs and GRAPHPLAN.
review of basic methods (Bayes and naive Bayes classifier, learning of trees and rules, handling noise, pruning of trees and rules), MDL principle, Support Vector Machines, evaluating success of learning and comparing learning algorithms, learnability and theoretical limits for learning.
Other paradigms of machine learning:
inductive logic programming, reinforcement learning, constructive learning and discovering new concepts with functional decomposition.
Reasoning with uncertainty:
reasoning and learning in Bayesian networks, construction of networks and causality.
Qualitative reasoning and modelling:
qualitative and quantitative modelling, modelling without numbers, qualitative simulation of dynamic systems.
Genetic algorithms, genetic programming and other problem-solving paradigms.