Course - Artificial Intelligence - IMT3104
IMT3104 - Artificial Intelligence
The course starts with a description of problem solving methods by means of heuristic search. Thereafter, various knowledge representation languages and inference methods for automatic problem solving. Representation in form of predicate logic, frames and semantic nets are treated, and connected to the main forms of reasoning - especially rule based reasoning. Furthermore, architectures that integrates various reasoning methods, agent based architectures and architectures for interactive problem solving. Numerous application examples are given to demonstrate the methods.
On successful completion of the module, students will be able to:
* Understand and evaluate various core techniques and algorithms of AI, including machine learning, tree and graph search algorithms, Markov decision process, Constraint satisfaction problems, and Bayesian networks. Understand the meaning of concepts such as intelligence, agents, reasoning, and making inferences.
* Identify different uses and applications of AI techniques and algorithms, from neuroscience, understanding brain to game development, to web technologies and secure system designs.
* Implement several of the algorithms on the coding games and different AI problems. The students will also enhance their programming skills in a preferred language of their own by learning to program AI algorithms and/or agents.
* Improve programming skills through the programming of AI algorithms and/or intelligent agents. Programming exercises and assignments help enhancing the understanding the theory learnt in class.
* Evaluate the run-time and memory complexity of several AI algorithms, and practice with creating better algorithms.
Learning methods and activities
Lectures, exercises, self-study and obligatory assignments.
This course will focus on the practical implementation of AI concepts. Lectures will introduce a topic area, and students are expected to implement and report on the key concept.
Further on evaluation
Written exam, 4 hours (60%) 4 compulsory assignments (40%). Each of these assignments must be passed individually to be able to take the written exam. Both parts must be passed.
Re-sit examination for the written exam may be changed to oral exam. The assignments must be taken the next time the course is running.
Supporting material allowed on exams: None
Admission to a programme of study is required:
Engineering - Computer Science (BIDAT)
Recommended previous knowledge
IMT2021 Algorithmic Methods
REA1101 Mathematics for computer science or REA2091 Mathematics 2 for computer science
To be announced.
- History and overview of AI
- Machine learning
- Markov decision process
- Game playing
- Constraint satisfaction problems
- Bayesian networks
- Other AI topics
Credits: 10.0 SP
Study level: Third-year courses, level III
Term no.: 1
Teaching semester: AUTUMN 2020
No.of lecture hours: 3
Lab hours: 2
No.of specialization hours: 7
Language of instruction: English
Examination arrangement: Portfolio assessment and Written exam
- Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
- Autumn ORD Portfolio assessment 40/100 ALLE
Room Building Number of candidates
- Autumn ORD Written examination 60/100 E 2020-12-11 09:00
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"