FI:IV126 Fundamentals of AI - Course Information
IV126 Fundamentals of Artificial Intelligence
Faculty of InformaticsAutumn 2024
- Extent and Intensity
- 2/0/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- doc. Mgr. Hana Rudová, Ph.D. (lecturer)
Mgr. Václav Sobotka (assistant) - Guaranteed by
- doc. Mgr. Hana Rudová, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics - Prerequisites
- The course is a continuation of the PB016 Introduction to Artificial Intelligence, PB016 completion is not a prerequisite for course completion.
It is presumed knowledge of graphs based on IB002 Algorithms and data structures I, and probability theory corresponding to the course MB153 Statistics I - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Formal analysis of computer systems (programme FI, N-TEI)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Informatics (programme FI, B-INF) (2)
- Informatics in education (programme FI, B-IVV) (2)
- Information Security (programme FI, N-PSKB_A)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computer Networks and Communications (programme FI, N-PSKB)
- Principles of programming languages (programme FI, N-TEI)
- Programming and development (programme FI, B-PVA)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Teacher of Informatics and IT administrator (programme FI, N-UCI)
- Informatics for secondary school teachers (programme FI, N-UCI) (2)
- Computer Games Development (programme FI, N-VIZ)
- Processing and analysis of large-scale data (programme FI, N-UIZD)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The course completes comprehensive introductory knowledge of artificial intelligence following the course PB016 Artificial Intelligence I. The course discusses search algorithms concentrating on metaheuristics and local search, classical planning, uncertain reasoning, and introduction into robotics oriented on robot path planning.
- Learning outcomes
- The graduate will be aware of local search and metaheuristics algorithms and will be able to solve practical problems with their help.
The graduate will understand problematics of the AI planning, will learn how to represent planning problem and how to solve it using base algorithms.
The graduate will gain an overview of how to work with uncertainties in the given problem and will learn to use basic procedures for including uncertainty in problem solving.
The graduate will be aware of the base concepts from robotics which is used for demonstration how the above knowledge can be applied, especially in the planning of robot motion. - Syllabus
- Local search and metaheuristics: Single-solution based search, principles, and concepts, strategies for improving local search. Population-based search, evolutionary algorithms, swarm intelligence.
- Planning: Problem representation. State space planning, forward and backward planning, and domain-specific planning. Plan space planning and partial order planning. Hierarchical task networks.
- Uncertain knowledge and reasoning: Probabilistic reasoning, Bayesian networks, exact and approximate inference. Time and uncertainty. Utility theory, decision networks. Sequential decision problems, Markov decision processes.
- Robotics: Robot hardware, robotic perception, and robot scheduling in manufacturing. Path planning in robotics, movement.
- Literature
- RUSSELL, Stuart J. and Peter NORVIG. Artificial intelligence : a modern approach. Fourth edition. Hoboken: Pearson, 2021, xvii, 1115. ISBN 9780134610993. info
- TALBI, El-Ghazali. Metaheuristics: From Design to Implementation. Wiley, 2009.
- GHALLAB, Malik, Dana NAU and Paolo TRAVERSO. Automated Planning: Theory & Practice. Morgan Kaufmann, 2004. info
- Teaching methods
- Standard lecture, one homework, and one written test during the semester. Lectures include exercises. Slides in Czech will be available from past semesters.
- Assessment methods
- Evaluation is completed based on the final written exam (70 points), one programming homework during the semester aimed to solve a practical problem (10 points), one written test during the semester (20 points), and bonus points for activity during lectures (about 12 points based on the number of lectures). Successful completion of the course requires getting at least 40 points for the written exam and at least 13 points for the work during the semester (homework, written test). Also, each student can get 1 bonus point for activity in each lecture (e.g., student response to several easy questions and/or student questions to clarify some part of the lecture or student response to one harder question). Evaluation is the following: A more than 90, B 89-80, C 79-70, D 69-60, E 59-55.
- Language of instruction
- English
- Further comments (probably available only in Czech)
- The course is taught annually.
The course is taught: every week. - Listed among pre-requisites of other courses
- Teacher's information
- https://is.muni.cz/el/fi/podzim2023/IV126/index.qwarp
Authenticated access see https://is.muni.cz/auth/el/fi/podzim2023/IV126/
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2024/IV126