DV2658 - Generative Artificial Intelligence - Blekinge Institute of Technology

1. Descision

This course is established by Dean 2024-10-31. The course syllabus is approved by Head of Department of Computer Science 2025-03-13 and applies from 2025-03-13.

2. Entry requirements

Admission to the course requires completed course in applied artificial intelligence, 6 credits, as well as taken course in machine learning, 6 credits.

3. Objective and content

3.1 Objective

The course provides an in-depth understanding of generative AI, focusing on image synthesis, anomaly detection, language processing, and multimodal learning. It covers fundamental principles, practical applications, and ethical aspects, as well as offering hands-on experience with modern tools and frameworks for developing and optimizing generative models.

3.2 Content

The course comprises the following topics:

Overview of generative AI and its applications
Generative AI models and neural architectures: applications for text, audio, images, and multimodal data
Techniques and data handling for the development and improvement of generative AI
Evaluation metrics and performance assessment for generative AI models
Ethical and societal implications of generative AI technology
Emerging trends and future directions in generative AI

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

Upon completion of the course, students will be able to:

Define and describe key concepts and techniques in generative AI, as well as explain its applications and methods for preprocessing, model design, and improvement.
Describe procedures and techniques for evaluating and enhancing the performance of generative AI models.

4.2. Competence and skills

On completion of the course, the student will be able to:

Develop, fine-tune, evaluate, and improve generative AI models for various applications.
Select, modify, apply, and evaluate appropriate generative AI tools, models, and frameworks based on task-specific requirements.

4.3. Judgement and approach

On completion of the course, the student will be able to:

Identify and address biases and ethical concerns in generative AI systems, considering their societal impact.

5. Learning activities

The content of this course will be covered through a series of lectures that introduce key concepts and methodologies in generative AI. Students are expected to supplement their learning through self-study of relevant literature. The course also includes lab sessions where students will practice implementing and applying generative AI techniques. The course includes a submission assignment where students apply basic generative AI models to specific tasks, assess the model's performance and limitations, and submit a report, code, and data for evaluation. In the group project, students design and implement solutions to real-world problems, evaluate the model's performance with relevant metrics, address biases and ethical issues, and fine-tune the models for specific applications. The project concludes with a presentation of the results, where students demonstrate their ability to develop and evaluate the solutions.

6. Assessment and grading

Modes of examinations of the course

Code	Module	Credit	Grade
2510	Written assignment	3 credits	AF
2520	Project Assignment	3 credits	GU

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Materials such as research articles and other course materials, as well as recommendations for additional reading, are provided via the courses’ online.

Course syllabus