Applied Artificial Intelligence2023/2024
- Purpose and learning objectives
The purpose of this elective is for the student to develop practical skills in applyingKnowledge
artificial intelligence, machine learning, and data science, to solve problems that can
not be solved using traditional software development methods
The student will gain knowledge on some of the most prominent areas of artificial intelligence:Skills
• deep learning
• supervised learning: classification and regression
• unsupervised learning: cluster analysis, anomaly detection, prototype/archetype analysis
• reinforcement learning: Q-learning and Deep Q-learning
• appropriate use of performance metrics
The student will understand and be able to reflect on questions like:
• what is artificial intelligence
• what kind of problems can be solved with artificial intelligence, and which models should be applied
The student will gain skills to:Competences
• solve practical problems using artificial intelligence and machine learning models
• identify and implement the most applicable models
• prepare data for use with machine learning models
• optimize models and solve common issues such as overfitting and underfitting
• apply performance metrics for model analysis
• communicate findings according to scientific standards
The student will learn to:
• develop solutions based on artificial intelligence that solves problems that can not be solved using traditional software development methods
• apply the scientific method to find solutions systematically
• formulate research questions, hypotheses, and null-hypotheses
• find and apply scientific articles for learning and to support own findings
- Type of instruction
The course is based on Problem-Based Learning. The students must apply knowledge from the classroom sessions in their project work, and acquire knowledge on their own to complete the projects. The teacher works in a supervisor role during project work, so the students are never left on their own.
- Subject/module requirement for
Equipment needed to participate
Windows PC, Mac or Linux
- min 8 GB RAM (16 GB RAM preferably)
- 200 GB available disk space
- CPU with virtualisation hardware (usually available in most modern CPUs)
- 15,6” screen or bigger is recommended
The learning outcomes of the exam are identical with the learning outcomes of the subject(s)/modul(es)Prerequisites for access to the examinationMandatory Assignment 1 and 2 approved.Exam in one or more subjectsSubject/module is tested standalone10 min individual presentation of the project, 15 min. examination in project and course curriculum.Type of examCombined written and oral examinationIndividual exam or group examIndividualExam languagesDanish (Norwegian/Swedish)DurationPresentation - max 10 min.
Examination- max. 15 min.
Grading - 5 min.
Total - 30 min.Permitted exam aidsComputerAvailable exam aidsProjectorType of evaluation7-point grading scaleExaminersInternal censure
- Preliminary literature list
This is a preliminary literature list. A final literature list will be provided in connection with study start.Course materials available to the students via Internet.
- Additional information
This elective subject may not run in certain semesters if not enough students choose it.
Some classes may take place online.
In the subject Applied Artificial Intelligence you will receive 80 hours of instruction, which corresponds to 106 lessons (1 lesson = 45 min.) and 29% of your total workload for the subject.
The teaching primarily consists of the following activities: classroom teaching, project work, peer-review, group work, Online teaching.
The preparation primarily consists of the following activities: project work, searching for information, reading your own notes, reading the curriculum.
Read about KEAs Study Activity Model
*KEA can deviate from the number of hours if this is justified by special circumstances