Course - Intelligent Machines - AIS4002
Intelligent Machines
Assessments and mandatory activities may be changed until September 20th.
About
About the course
Course content
The course contains a selection of some of the following topics within artificial intelligence (AI), with application towards intelligent machines:
- introduction to AI and intelligent agents
- uninformed search algorithms (e.g., DFS, BFS, UCS, IDS)
- informed (heuristic) search algorithms (e.g., greedy BFS, A*)
- adversarial search (e.g., minimax, expectimax, expectiminimax, alpha-beta pruning)
- simulation of models for problem-solving
- optimization problems and intelligent optimization algorithms (e.g., evolutionary algorithms, particle swarm optimization, etc.)
- constraint satisfaction problems (CSP)
- artificial neural networks
- reinforcement learning
- fuzzy expert systems
- agent-based modelling and simulation
- hybrid intelligent systems
- machine vision
- possibly other topics
More details about the curriculum will provided during the start of semester.
This course can also be taken by exchange students or other students not enrolled in the study programme, provided they have a suitable background and permission is granted by the course coordinator.
Learning outcome
Knowledge and skills
Upon completion of the course, students can do the following in the context of AI andintelligent machines:
- describe AI in terms of the analysis and design of intelligent agents or systems that interact with their environments
- explain relevant AI terminology, models, and algorithms used for problem-solving, as well as limitations and risks
- model problems in suitable state space depending on choice of solution method
- simulate models and solve real-world problems by means of appropriate choice of AI methods
- use AI methods for cyber-physical systems
- analyse models, methods, simulations, and physical tests and results
Competence
Upon completion of the course, students can
- consult reliable sources on AI and present real-world problems, choice of methods, and results in a short, concise manner
- discuss and communicate advantages and limitations of selected AI methods for problem-solving in a scientific manner
- reflect upon and discussopportunities and threats of AI in human society, and what measures may be required to make AI beneficiary to human society, including aspects of ethics and sustainability
Learning methods and activities
Learning activities generally include a mix of lectures, seminars, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.
Compulsory assignments
- Compulsory activities
Further on evaluation
The final grade is based on an overall evaluation of the portfolio, which consists of work that is carried out, documented and digitally submitted during the term. Such submissions may include some of the following:
- software
- technical reports
- essays
- reflection notes
- video submissions, e.g. demonstration of work or tests of knowledge
- possibly other kinds of submissions
Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio.
The re-sit exam is an oral exam in August.
Note that the course also has some compulsory activities that must be approved in order for the portfolio to be assessed.
More information will be provided at the start of the course.
Specific conditions
Admission to a programme of study is required:
Mechatronics and Automation (MSMECAUT)
Required previous knowledge
he course has no prerequisites. It is a requirement that students are either (a) enrolled in the study programme to which the course belongs, or (b) taking the course as an exchange student. Other exceptions may be made upon approval from the academic director of the study programme.
Course materials
An updated course overview, including curriculum, is presented at the start of the semester.
Credit reductions
Course code | Reduction | From |
---|---|---|
IE502014 | 7.5 sp | Autumn 2024 |