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Blekinge Institute of Technology
Department of Technology and Aesthetics
Revision: 1
Reg.no: BTH-4.1.14-0646-2024
Course syllabus
Applied generative AI
Applied generative AI
6 credits (6 högskolepoäng)
Course code: DV1712
Main field of study: Computer Science, Media Technology
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G1F - First cycle, has less than 60 credits in first-cycle course/s as entry requirements
Language of instruction: Teaching is given in English
Applies from: 2024-08-15
Approved: 2024-08-15
1. Descision
This course is established by Dean 2024-04-30. The course syllabus is approved by Head of Department of Technology and Aesthetics 2024-08-15 and applies from 2024-08-15.
2. Entry requirements
Admission to the course requires 30 completed credits in the main field of study Computer Science or Media Technology.
3. Objective and content
3.1 Objective
The aim of the course is for students to explore the basics of generative AI and large language models (LLMs) with a practical focus. The course covers the creation of text, images and other outputs of these models. The student examines features, architecture, training processes and practical implementation methods.
3.2 Content
- Introduction to AI with a focus on neural networks and generative AI
- Overview of applications (text generation, media synthesis, code generation, data synthesis)
- History and trends, competing research trends
- Core mechanisms and concepts of generative AI and LLMs
- Tokenization, embeddings, attention mechanism, transformers architecture, zero-shot and few-shot learning, foundational models, self-supervised learning, multi-modal interaction
- Fine-tuning and deployment
- Using open-source tools and libraries for text and media generation
- Techniques for fine-tuning models for specific tasks
- Deployment process for generative AI models
- Practical applications and case studies
- Case studies on successful deployments of generative AI
- Enhancing digital experiences with AI-generated content
- Ethical considerations and responsible AI
- Biases and other limitations of generative AI
- Ethical implications of generative AI
- Legal challenges and societal impacts
4. Learning outcomes
The following learning outcomes are examined in the course:
4.1. Knowledge and understanding
**On completion of the course, the student will be able to:
- grasp the basics of generative AI and LLMs
- comprehend the architectures and training processes of LLMs
- account for understanding trends and potential future changes in generative AI
4.2. Competence and skills
On completion of the course, the student will be able to:
- gain hands-on experience in setting up environments to use open-source models
- learn techniques for fine-tuning and deploying generative AI models
- apply theoretical knowledge in practical projects and labs
4.3. Judgement and approach
On completion of the course, the student will be able to:
- Critically assess the impact and limitations of generative AI and LLMs
- Understand trends and potential future changes in generative AI
- Evaluate ethical implications and develop responsible AI practices
- independently and critically evaluate their creative and aesthetic applications of generative AI
5. Learning activities
The course includes lectures, exercises, laboratories, and projects. Lectures provide the theoretical foundations essential for course understanding. Exercises, laboratories, and projects offer practical application of theoretical concepts, with group-based projects and laboratory work. The course also necessitates students to utilize their problem-solving skills to address challenges encountered during learning activities
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2505 | Written assignment | 1.5 credits | GU |
| 2515 | Project | 3 credits | AF |
| 2525 | On-campus Examination | 1.5 credits | AF |
The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Fail, supplementation required, F Fail.
The information before a course occasion 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
Course material from the department selected journal papers
This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.