IKT526 Emerging AI technologies (Autumn 2025)
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Manuel Sathyajith Mathew
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- ½ year
Table of contents
The course is connected to the following study programs
- Artificial Intelligence and The Internet of Things, Master's Programme
- Artificial Intelligence, 5-year master programme
Teaching language
Recommended previous knowledge
Learning outcomes
Contents
The course will introduce the students to advanced AI models and how to implement them through programming. In the lab, students will learn different techniques such as tokenizing, data analysis, etc.
The course will introduce the students to advanced concepts in Gen Artificial Intelligence and to illustrate clearly the way to implement these AI models.
Quick Recap of Supervised and Unsupervised learning, (Image Generation), Text to image: Stable diffusion, etc. Basic of NLP, Different LLM Models and its component (Data Preparation, Training optimization, Sampling techniques, Quantization), RAG Model and its component (Indexing, Data vectorization, Retrievers), Data Prediction: Preprocessing, Different models such as Seq to Seq, Informer, PSF, diff preprocessing Hybrid AI models.
Teaching and learning methods
The course introduces the students to advanced AI models and how to implement them through programming. In the lab, students learn different techniques such as tokenizing, data analysis, etc.
The course is organized in combination of lectures, assignments, paper studies, labs, report writing and self-study. The tasks are done individually or in small groups with group supervision.
The workload for the average student is approximately 200 hours.