Course syllabus - Artificial Intelligence 1

Objectives

The course aims to lay a foundation for the subject of artificial intelligence through both theory and practice. The course also aims at providing case studies in the related area.

Learning outcomes

After completing the course, the student should be able to: 

1. define a problem, and its search space, as well as understand how different representations of the problem, based on states, can improve the performance of an algorithm that solves the problem,  
2. apply the most representative uninformed search strategies in a state search space and analyse their efficiency in time and space,  
3. apply the most representative informed search strategies in a state search space and analyse the consequences of heuristic functions on time and space efficiency,  
4. understand how to solve a constraint satisfaction problem and apply algorithms to deal with constraints,  
5. apply well-known optimization techniques,
6. use adversarial search techniques and understand their relation to games,  
7. analyse a given problem, evaluate if it can be addressed by an algorithms that is reviewed  in this course  and also
8. carry out an ethics and gender equality analysis of data and its impact on an AI system.

Course content

- Introduction to artificial intelligence. 
- Types of problems, in which AI can be applied and how to define them. 
- Search algorithms, such as Breath-first, Depth-first, and A* (other methods may be included based on the context). 
- Trajectory-based optimization, such as Hill climbing and simulated annealing (other methods may be included based on the context). 
- Adversarial search, such as minimax and alpha-beta pruning (other methods may be included based on the context), and games. 
- Constraint satisfaction problems.
- Ethics and gender equality analysis of AI systems with a focus on data.

Specific requirements

Computer Programming with Python 7.5 credits and also Data Structures, Algorithms and Program Development with Python 7.5 credits or corresponding.

Examination

Laboratory work 1 (LAB1), an assignment that is presented with a report and a demonstration to the teacher, 2 credits, examines the learning objectives 1, 2, 3 and 7, marks Fail (U), 3, 4 or 5.  

Laboratory work 2 (LAB2), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 1, 4, and 7, marks Fail (U), 3, 4 or 5. 

Laboratory work 3 (LAB3), an assignment that is presented with a report and a demonstration to the teacher, 2.5 credits, examines the learning objectives 1, 5, 7 and 8, marks Fail (U), 3, 4 or 5. 

Laboratory work 4 (LAB4), an assignment that is presented with a report and a demonstration to the teacher, 1.5 credits, examines the learning objectives 1, 6 and 7, marks Fail (U), 3, 4 or 5.

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass with distinction, Pass with credit, Pass, Fail

Interim Regulations and Other Regulations

The course completely overlaps towards CDT312/DVA340/DVA349 Artificial Intelligens and DVA251 Artificial Intelligence 1. The courses Artificial Intelligence 1 and Artificial Intelligence 2 can not be included in the same degree as CDT312/DVA340/DVA349 Artificial Intelligence.
 
The course contributes to fulfill the degree requirement of at least 75 credits in the main area of computer science with the focus of intelligent systems for technology bachelor's degree in computer science with the focus of intelligent systems.