Messages

Here you can find solution hints for the first exam.

Grading

Students will work on 3 mini-projects which will cover 3 units: problem solving by searching, inference in predicate logic and learning from data. Each mini-project will be scored up to 15 points. The course is completed with a final exam which is scored up to 55 points. The final grade is based on the total number of points. To pass the exam, it is necessary to obtain minimum 10 points. Under that condition, the final grade is obtained according to the following rules:
5.0: 91-100 points
4.5: 81-90 points
4.0: 71-80 points
3.5: 61-70 points
3.0: 51-60 points
2.0: 0-50 points
If the exam is not passes, the final grade is 2.0, regardless of the total number of points obtained.

Reading

G. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Addison-Wesley, 2008.

Z. Michalewicz, D. Fogel: How to solve it: modern heuristics, Springer, 2004.

S.Russell, P.Norvig, Artificial Intelligence: a modern approach, Prentice Hall, 2002.

http://www.roughsets.org

See also

• Messages
• Grading
• Reading
• Syllabus
• Courses

Syllabus

unit	contents
1	Weak and strong AI, concept of problem solving by searching, relation between the formulating the problem as a search task and its solvability
2	Depth-first and breadth-first search. Best-first search. Heuristic function. A* method.
3	Predicate logic - basic concepts: formulas, logical functions, variables, relation to the set theory, normal forms. PROLOG as an example system of inference in predicate logic: example predicates, inference mechanism, substitution and unification Inference in predicate logic as a search task: space of proofs, depth-first and breadth-first search
4	Learning from data: taxonomy of learning tasks, measures of quality, overview of methods to represent knowledge Learning rules from data: Rough Set methodology. Indiscernibility relation, concepts of reduct and core indescernibility matrix, reduct derivation, decision rules inference as a searching task. Learning decision trees from data, overview of problems with unbalanced attribute domains and examples, overlearning. Ensembles of learning systems: idea of bootstrapping, reducing generalization error by bootstrapping, random forests.
5	Decision making based on models.