5042 - Introduction to Artificial Intelligence
Course information
- Title
- Introduction to Artificial Intelligence
- Course number
- 5042.22
- Academic year
- 2024-2025
- ECTS
- 7.50
- Level
- Bachelor
- Faculties
- Science and Tecnology
- Educations
- BSc in Software Engineering
- Prerequisites
- Discrete Mathematics (7.5 ECTS), Algorithms and data structures (7.5 ECTS), Introductory programming with Python (7.5 ECTS)
- Language of instruction
- The course is taught in Swedish and English. The textbook is in English and other instructional materials are in English, Faroese and possibly Scandinavian languages. Exams may be in Faroese, Danish or English.
- Registration
- Students on the fifth semester of B.Sc. in Software Engineering are automatically enrolled. Applicants for an individual course must apply via the Student Service Center at lss@setur.fo
- Beginning date
- Monday, November 4, 2024
- End date
- Friday, January 17, 2025
Academic content
- Purpose
- The objective of this course is to introduce the basic ideas and intuition behind the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modelling paradigm.
- Learning outcomes
- By the end of the course, the student is expected to be able to: - Explain, apply, and implement uninformed and informed search techniques to solve AI problems. - Explain, apply, and implement artificial intelligence search techniques in games. - Design, implement, benchmark, and analyse search heuristics and game evaluation functions. - Explain, design, and implement intelligent systems that draw inferences in uncertain environments and optimise actions for arbitrary reward structures. - Design, and develop an autonomous agent that efficiently makes decisions in fully informed, partially observable, and adversarial settings. - Correctly identify the relevant AI technique for solving a given problem. - Explain the strengths and weaknesses of different AI techniques in search and planning, probabilistic reasoning, and machine learning.
- Content
- What is AI? The Foundation of Artificial Intelligence. Planning agents. Uninformed searching strategies. Informed/Heuristic search strategies: A*, Greedy search. Nondeterminism and partially observable. Adversarial Search: Game trees, Minimax. Pruning techniques. Solving Markov decision processes. Bayesian network representation and assumptions. Inferences and sampling techniques. Supervised learning. Neural networks. Convolutional neural networks. Applications and limitations of neural networks. Reinforcement Learning, Model-based learning. Model-free learning. Q-learning.
- Learning and teaching approaches
- Lectures, exercises, home assignments, self-studies and group work.
Assessment
- Assessment method
- A 4-hour written exam. All three mandatory assignments must be passed to be eligible for the examination and re-examination.
- Examination (internal/external)
- External
- Grading scale
- 7-scale
- Exam date/dates
- The written exam is set for week 3, 2025
- Deadline for withdrawal from exam
- Monday, November 4, 2024
Academic responsibility and teachers
- Academic responsibility
- Jákup Odssonur Svøðstein
- Teachers