Course

Generative AI (DAT560)

Facts

Course code DAT560

Credits (ECTS) 10

Semester tuition start Spring

Language of instruction English

Number of semesters 1

Exam semester Spring

Time table View course schedule

Literature Search for literature in Leganto

Introduction

This course provides an in-depth understanding of generative AI, covering key concepts, techniques, and applications. It explores model development for generating text, images, and media, with advanced topics like multimodal AI and ethical considerations such as fairness and bias.

Content

This course offers a deep understanding of generative artificial intelligence (AI) fundamentals, covering foundational concepts, key techniques, and a wide range of applications. Students will learn about the core principles that underpin generative AI, including the development and evaluation of models capable of creating text, images, and other forms of media. The course also introduces advanced topics, such as the integration of text and visual data, and the application of generative AI across different domains like medicine, law, and creative industries. Ethical considerations, such as fairness and bias, are emphasized throughout the course, ensuring students are prepared to build responsible AI systems.

Learning outcome

Knowledge:

  • Understand the fundamental concepts and principles of generative AI.
  • Ability to identify the need and use generative AI in diverse domains and applications.
  • Grasp the ethical challenges associated with generative AI, including issues of fairness and bias.

Skills:

  • Develop and refine generative AI models for various applications, with an emphasis on practical implementation.
  • Evaluate the effectiveness and impact of generative AI systems in different contexts.
  • Design and build generative AI applications, considering the entire development lifecycle from concept to deployment.

General Competencies:

  • Apply generative AI techniques to solve real-world problems in a variety of domains.
  • Critically assess the societal implications of generative AI, particularly in terms of ethics and responsibility.
  • Collaborate effectively with interdisciplinary teams to innovate in the field of generative AI

Required prerequisite knowledge

None

Recommended prerequisites

Programming fundamentals (DAT120), Probability and Statistics 1 (STA100)
Python, Jupyter notebooks, pandas, scikit-learn, pytorch, tensorflow.

Exam

Project report + code + oral presentation and written exam

Project report + code + oral presentation

Weight 2/5

Marks Letter grades

Exam system Canvas

Written exam

Weight 3/5

Duration 4 Hours

Marks Letter grades

Exam system WISEflow

Project consisting of one large assignment (40% of the grade) done over 6 weeks. The project is to be performed in a group. The grade for the project will be based on the submitted program code, project report document and an oral presentation in groups. Both parts must be done before final grade is given.

If a student fails the project, she/he has to take this part next time the subject is lectured.

The written exam (60% of the grade) will be digital.

Both project and written exam must be passed in order to receive a final grade in the course.

Coursework requirements

Compulsory assignments

3 compulsory programming assignments ungraded (pass or fail) divided among fundamentals, LLMs and vision modules.

All programming exercises must be passed to attend for the written exam and to get project approved. Completion of mandatory lab assignments are to be made on time. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon. Failure to complete the assigned labs on time or not having them approved will result in barring from taking the exam of the course. Manual approval of the assignments based on the demonstration of deeper understanding of the assignment solution is compulsory.

Method of work

4 hours lectures/exercises and 2 hours of guided programming exercises and project. Programming exercises requires additional non-guided work effort.

Using AI to support learning and study activities

AI tools may be used in DAT560 as a learning aid, but must never substitute for the understanding of generative AI the course builds — understanding the principles behind generative AI and owning the code, judgements and ethical reflection in the project yourself is precisely what you must master. Productive uses include asking AI to explain a concept (e.g., how a generative model is trained, what distinguishes generative AI from discriminative models, or an ethical challenge of generative AI), to review or debug code you have written yourself, or to suggest tests and edge cases. Any use of AI must always be disclosed in your submission as specified in the syllabus, and you remain responsible for critically evaluating whatever the AI produces.

Open for

Admission to Single Courses at Master Level at the Faculty of Science and Technology
Data Science - Master Computer Science - Master Cybernetics and Applied AI - Master
Exchange programme at The Faculty of Science and Technology

Admission requirements

Must meet the admission requirements of one of the study programmes the course is open for.

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.

Contact

Head of Department:
Tom Ryen
Course coordinator:
Vinay Jayarama Setty
Course teacher:
Petra Galuscakova

Offered by

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1