Course syllabus - Project in applied artificial intelligence

Objectives

This will provide knowledge how to plan, conduct and present a project within the area of artificial intelligence, I.e., to identify and apply AI methods to solve a problem. To complete the project the student is expected to utilize and integrate previously acquired knowledge, experiences, and skills, and thereby deepen the understanding of the field of study. The purpose of the project work is for the student to develop and show knowledge and ability as well as serve as a preparation for the thesis.

Learning outcomes

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

1. from a given problem formulate a solution that contains AI and reflect over alternative solutions, 
2. from a given problem select and implement an AI algorithm/system, 
3. perform a critical review of and provide constructive feedback on other students' work, 
4. present a solution both orally and in written form in a short and concise report capturing implementation, results, and conclusions from the project, 
5. apply scientific methods and approaches and relate the solutions to the existing research results and methods, 
6. carry out an ethical and gender equality analysis of data and the AI system in the project and also
7. carry out a gender equality analysis of the project work.

Course content

The course deals with aspects from problem formulation to implementation of an intelligent system including methods and techniques from artificial intelligence: 
- problem formulation 
- identification of different AI methods to solve a specific problem 
- how to implement AI methods for different applications 
- how to explain and describe an AI based system 
- how to carry out an ethical and gender equality analysis of an AI-system

Specific requirements

Completed courses of 90 hp in computer science including Artificial Intelligence 1 7,5 credits, Artificial Intelligence 2 7,5 credits, smart digital platforms: Cloud Computing, Security and Big Data 7,5 credits, Data management and datafication 7,5 credits and deep learning  and neural network 7,5 credits, or corresponding courses.

Examination

Written assignment (INL1), 4 credits, examines the learning outcomes 1 and 5, marks Fail (U) or Pass (G).
Seminar (SEM1), 1 credit, examines the learning outcome 1, marks Fail (U) or Pass (G).
Seminar (SEM2), 1 credit, examines the learning outcome 3, marks Fail (U) or Pass (G).
Seminar (SEM3), 1 credit, examines the learning outcome 4, marks Fail (U) or Pass (G).
Project (PRO1), 8 credits, examines the learning outcomes 1-7, 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 with CDT406 Applied 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.