IKT636 Current Research Advances in Al (Autumn 2025)
- ECTS Credits:
- 5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Lei Jiao
- Lecture Semester:
- Autumn and Spring
- Teaching language:
- English
- Duration:
- ½ year
Table of contents
The course is connected to the following study programs
Teaching language
Prerequisites
Recommended previous knowledge
Learning outcomes
Upon completion of the course, students will be able to:
1. Understand the latest research advancements in the field of Al, with a particular focus on the areas of expertise and ongoing research conducted by professors at UiA.
2. Analyze and incorporate the aforementioned research advancements into the PhD candidates' own research within their respective fields.
Contents
The course covers the following topics:
1. Al in Cybersecurity
2. Explainable Al
3. Attention Mechanisms and Transformers in Deep Neural Networks
4. Vision Language Models
5. Deep Reinforcement Learning
6. Clustering Methodsand Learning Automata-Based Clustering
7. Introduction to the Tsetlin Machine
8. Theoretical Foundations and Applications of the Tsetlin Machine
9. Industry applications of Al
Teaching and learning methods
Examinations
To successfully complete the course, students must actively engage in course components, deliver an oral presentation with a Q&A session, and submit a comprehensive project report.
The course assessment consists of:
Oral Presentation: Evaluated based on the clarity of communication, understanding of theoretical concepts, and ability to engage in discussion during the Q&A session.
Project Report: Assessed for coherence, analytical approach, and relevance, demonstrating the candidate's ability to apply key course concepts. The assessment ofthe examination is passed/ not passed.