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:

• Heuristics for search.
• Search for games.
• Applied first-order logic.
• Deterministic planning.
• Knowledge representation.
• Probability theory: axioms, conditional probability, Bayes' rule.
• Bayesian networks.
• Probabilistic reasoning over time.
• Hidden Markov Models: Stochastic planning.
• Decision trees.
• Reinforcement learning.
• Multi-agent systems.
• Multi-criteria decision-making, e.g., utility functions, evaluation of alternatives, pareto-optimality.

Part 2, laboratory, 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:

• Describe and apply concepts, methods, and theories of search, heuristics, games, knowledge representation, planning (FSR 1)
• 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 2)
• 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) (FSR 3)

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

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

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

• Discuss the effect on society of emerging technologies in AI (FSR 7)
• Evaluate different AI-based technologies using the knowledge gained during the course (FSR 8)

Required Knowledge

Entry requirements include either a bachelor's degree in Computer Science or:
- at least 90 ECTS of which at least 60 ECTS within Computer Science, or at least 120 ECTS within a study programme
- Fundamentals of Artificial Intelligence, 7.5 ECTS, or equivalent
- at least 7.5 ECTS in data structures and algorithms
- at least 7.5 ECTS in logics

Proficiency in English equivalent to Swedish upper secondary course English B/6. 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 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. In order to pass the course completely, all mandatory parts must be passed.

Support due to disability
Deviations from the syllabus' modes of assessment can be made for a student who has a decision on pedagogical support due to a disability. Individual adaptation of modes of assessment must be considered based on the student's needs. The mode of assessment is adapted within the framework of the syllabus' expected learning outcomes. At the request of the student, the course coordinator, in consultation with the examiner, shall promptly decide on an adapted mode of assessment. The decision must then be notified to the student.

Change of examiner
A student who, without receiving a passing grade, has participated in two tests for a course or part of a course, has the right to have another examiner appointed, unless special reasons militate against it (Högskoleförordningen 6 kap. 22 §). A request for a new examiner is made to the head of the Department of Computing Science.
 

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.