Upon completion of this course, students will have an overview of different topics in Artificial Intelligence. They will be able to:
- Understand, and are able to reason with, basic aspects of computational complexity theory such as NP-hardness and reductions;
- Understand, apply, and implement search algorithms for finding shortest paths, maximum flow, and minimum spanning trees;
- Understand the simulated annealing heuristic for local search;
- Understand the Monte Carlo Tree Search strategy for adversarial search;
- Understand, apply, and reason about planning, optimization, and scheduling problems, using various representations (feature based, STRIPS, constraint satisfaction) and optimization strategies (regression-based, forward-based, CSP resolution, and linear programming);
- Understand, and be able to compute and reason with probabilities, preferences and utilities;
- Understand, apply, and implement the Variable Elimination algorithm for inference;
- Understand approximation, learning, and decision making in Bayesian Networks;
- Understand, apply, and reason with important aspects from Markov Decision Processes and Machine Learning, such as model based versus model free learning, exploration and exploitation, on- and offline learning, and the Markov property;
- Understand important algorithms such as value and policy iterations, and their mathematical background, in particular the Bellman equations.
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This course introduces students to the important aspects of the symbolic approach to Artificial Intelligence. During the course, students will gain insight into those important issues from the "classic" AI that are relevant to modern developments.
The main themes of the course are: complexity of problems and algorithms, search strategies and algorithms, optimization and scheduling algorithms, planning problems, reasoning with uncertainty (Bayesian networks), decision making and machine learning (especially reinforcement learning).
Once they have completed the course, students will be able to apply these techniques to simple practical examples. |
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| Students will need to use the knowledge they obtained in the first year of the Bachelor Artificial Intelligence programme in the courses Programming 1 (BKI131), Programming 2 (BKI132) and Introduction to Formal Reasoning (NWI-IPK0001).
Equivalent course for Programming 1 and 2: NWI-IPC031.
Equivalent courses for Formal Reasoning are: NWI-IPC002 Languages and Automata and NWI-IPI004 Logic and Applications. |
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The examination is comprised of two parts:
- Four programming assignments (with varying weight, but 0.3 in total);
- Two partial exams. (each with weight 0.35).
The final mark will consist of a weighted average mark for the programming assignments and partial exams, for which a minimum mark requirement has been set. Both partial exams may be retaken and assignments may also be resubmitted, if necessary. The grade for the assignments in Osiris represents the final weighted grade after resubmissions. Make sure to register for retaking the partial exams! Please see Brightspace for further details.
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