Artificial Intelligence - Methods and Applications, 7.5 Credits

Contents

Part 1, theory, 4.5 credits. The course provides theoretical and methodological knowledge and skills in classical AI (artificial intelligence) and robotics. Topics covered: Heuristics for search. Search for games. Applied predicate logic. Classical planning: heuristics. Knowledge representation. Probability theory: axioms, conditional probability, Bayes’ rule. Bayesian networks. Probabilistic reasoning over time, Hidden Markov Models. Decision trees and learning. Robotics: reinforcement learning, learning from demonstration, hybrid architectures, motion planning, odometry, metric and topological route planning, localization and map generation.

Part 2, laboratory, 3 credits. In the laboratory part some of the theories and techniques discussed in the theoretical part are put into practice. This part consists of a series of mandatory laboratory assignments, in part carried out with physical robots or advanced simulators.

Expected learning outcomes

After having completed the course the student will be able to:
- design, construct, and evaluate intelligent agents
- describe and apply concepts, methods, and theories of search, heuristics, games, knowledge representation, planning
- describe and apply concepts, methods, and theories of logic and probability theory and to analyze the power and limitation of their use for knowledge representation and reasoning systems
- describe methods and theories of Bayesian networks, probabilistic reasoning over time and Hidden Markov Models, decision trees, and learning
- describe and apply methods and theories for hybrid architectures, odometry, motion planning, topological and metric route planning andtrackinglocalization, and map generation

Required Knowledge

To be admitted you must have 60 ECTS-credits in Computing Science or 2 years of completed studies, in both cases including the courses Fundamentals of Artificial Intelligence (5DV121), Data Structures and Algorithms (5DV108/5DV127/5DV128), Fundations of Logic and Model Theory (5DV102) or equivalent. Proficiency in English equivalent to Swedish upper secondary course English A (IELTS (Academic) with a minimum overall score of 5.5 and no individual score below 5.0. TOEFL PBT (Paper-based Test) with a minimum total score of 530 and a minimum TWE score of 4. TOEFL iBT (Internet-based Test) with a minimum total score of 72 and a minimum score of 17 on the Writing Section).
Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Form of instruction

Instruction consists of lectures and mandatory computer based assignments. In addition to scheduled activities, individual work with the material is also required.

Examination modes

The examination consists of a written exam in Part 1 and by grading the mandatory assignments in Part 2. In Part 1, the grades given are Fail (U), Pass (3) or Pass with Mark (4), or Pass with Distinction (5). In Part 2 the grades given are Fail (U) or Pass (G). On the course as a whole, the grades given are Fail (U), Pass (3) or Pass with Mark (4), or Pass with Distinction (5). In order to pass the course as a whole, all mandatory parts must be passed as well. The final grade of the course is a summary assessment of the results and decided only after all mandatory parts are passed.

A student who has passed an examination may not be re-examined.

For students who do not pass the regular examination there is another opportunity to do the examination. A student who has failed two examinations for a course or segment of a course, has the right to have another examiner appointed, unless there are special reasons (Higher Education Ordinance Chapter 6, section 22). Requests for new examiners are made to the head of the Department of Computing Science.

TRANSFER OF CREDITS
In an exam, this course may not be included, in whole or in part, together with another course of similar content. If in doubt, consult the student counselors at the Department of Computing Science and / or the program director of your program.

Note that this course can not be fully included in an examination together with one of the courses Intelligent Robotics (5DV053) or Artificial Intelligence (5DV019).

Transfer of credits is considered individually (see the University Code of Rules and regulations for transfer of credits). An application for transfer of credits is made on a special form and should be submitted to the Faculty of Science and Technology, Umeå University.