1. define the basic concepts of artificial intelligence
2. distinguish between symbolic and connectivistic approaches to AI
3. apply state search algorithms and biologically inspired optimization algorithms on basic problems
4. solve basic problems using logic programming
5. apply inference algorithms on basic logical problems
6. compare among various approaches to representing uncertainty
7. assess the applicability of different AI methods on a given AI problem
8. apply the basic machine learning algorithms
9. review the philosophical aspects of artificial intelligence

Weekly three-hour lectures (both ex-cathedra and flipped-classroom style)

Four programming assignments

1. AI problems and applications, AI definitions and Turing test, Agents and environments
2. State space search problem, Uninformed search (breadth-first, depth-first, depth-first with iterative deepening)
3. Heuristics and informed search (hill-climbing, generic best-first), Minimax search and alpha-beta pruning, Constraint satisfaction (backtracking and local search methods), A* search, beam search
4. Logic as a knowledge representation scheme (ontological and epistemological commitments), Formalizing natural language sentences in predicate logic, Resolution rule for propositional logic, Resolution rule for predicate logic
5. Logic-based expert systems, Reduction to logic programming
6. Description logics and ontologies, Semantic networks, Non-monotonic reasoning, Spatial-temporal reasoning
7. Rule-based reasoning, Case-based and model-based reasoning, Planning, Rule-based expert systems
8. Midterm exam
9. Certainty factors, Fuzzy sets and fuzzy logic, Fuzzy logic inference (fuzzy propositions, fuzzy relations, and fuzzy implications), Fuzzy inference engines; fuzzyfication and defuzzyfication
10. Probabilistic frameworks (Bayesian networks, Markov networks), Bayes inference
11. Machine learning tasks and applications, Machine learning approaches and paradigms, Naïve Bayes classifier, Decision trees (ID3, C4;5)
12. Environment, reward and value functions, Markov decision processes (MDP), Approximate dynamic programming methods (Q-learning)
13. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning), Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time)
14. Philosophical issues
15. Final exam