Artificial Intelligence - Methods and Applications, 7.5 Credits

Contents

Modern intelligent systems are transforming the daily life of society. These kind of systems are designed and implemented considering Artificial Intelligent (AI) models and algorithms. This course aims to present different AI theories and algorithms in order to give a solid background in the area, as well as practical knowledge about how to implement real intelligent  systems. The main theme of the course is theories and algorithms from classical AI. During the course, the students will acquire knowledge about different paradigms of AI, e.g. logic-based and data-driven methods as well as rational intelligent agents.
 
The course consists of two parts:
Part 1, theory, 4.5 credits
Topics covered:

• Search algorithms, e.g., adversarial search and games.
• Answer Set Programming (ASP), e.g., stable models, optimization modelling.
• Knowledge representation, e.g., description logics.
• Probability theory, e.g., axioms, conditional probability, Bayes' rule.
• Probabilistic reasoning, e.g., Bayesian networks.
• Probabilistic reasoning over time, e.g., hidden markov models.
• Sequential decision making, e.g., markov decision processes, stochastic planning.
• Reinforcement learning.
• Agent architectures, e.g., BDI agents.
• Multi-criteria decision-making, e.g., utility functions, evaluation of alternatives, pareto-optimality.

Part 2, practice, 3 credits.
In the laboratory part some of the theories and techniques discussed in the theoretical part are put into practice.

Expected learning outcomes

Knowledge and understanding
After having completed the course the student should be able to:

• (FSR 1) describe and apply concepts, methods, and theories of search, heuristics, games, knowledge representation, planning;
• (FSR 2) 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;
• (FSR 3) describe methods and theories of probabilistic reasoning (e.g. Bayesian networks), probabilistic reasoning over time (e.g. Hidden Markov Models), Probabilistic Planning (e.g. Markov Decision Processes), and learning methods (e.g. decision trees, reinforcement learning, Bayesian learning).

Skills and abilities
After having completed the course the student should be able to:

• (FSR 4) design, construct, and evaluate intelligent software agents;
• (FSR 5) apply concepts for adversarial search and construct good heuristics;
• (FSR 6) demonstrate theoretical and practical skills in developing intelligent software agents based on data-driven and logic-based methods.

Values and attitudes
After having completed the course the student should be able to:

• (FSR 7) discuss the effect on society of emerging technologies in AI;
• (FSR 8) evaluate different AI-based technologies using the knowledge gained during the course.

Required Knowledge

At least 90 ECTS, including 60 ECTS Computing Science, or at least 120 ECTS within a study programme. At least 7.5 ECTS data structures and algorithms; 7.5 ECTS artificial intelligence; 7.5 ECTS discrete mathematics; and 7.5 ECTS logics. Proficiency in English equivalent 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 of Part 1 (FSR 1-3, 7-8) consists of a written exam in halls. The grades given are Fail (U), Pass (3), Pass with merit (4), or Pass with distinction (5).
 
The examination of Part 2 (FSR 4-6) consists of mandatory assignments, each of which is described in a written report. In Part 2 the grades given are Fail (U), Pass (G).
 
On the course as a whole, the grades given are Fail (U), Pass (3), Pass with merit (4), or Pass with distinction (5). The overall grade is primarily based on the written exam.

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

In an exam this course may not be included, in whole or in part, simultaneously with another course of similar content. If in doubt, consult the student counselors at the Department of Computing Science.

Specifically, this course cannot be included in a degree together with 5DV122 Artificial Intelligence - Methods and Applications. The overlap between the courses is 7.5 credits.

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.