Course

Generative AI (DAT560)

Fakta

Emnekode DAT560

Vekting (stp) 10

Semester undervisningsstart Spring

Undervisningsspråk English

Antall semestre 1

Vurderingssemester Spring

Timeplan Vis timeplan

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Intro

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

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

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

Eksamen / vurdering

Project report + code + oral presentation

Vekt 2/5

Karakter Letter grades

Written exam

Vekt 3/5

Varighet 4 Hours

Karakter Letter grades

Eksamenssystem 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 hearing in groups of the submitted program code and report. 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 (Inspera).

Both exam units must be passed in order to receive a final grade in the course.

Vilkår for å gå opp til eksamen/vurdering

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.

Åpent for

Admission to Single Courses at Master Level at the Faculty of Science and Technology
Data Science Computer Science Cybernetics and Applied AI
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.

Emneevaluering

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.

Litteratur

Book Generative deep learning : teaching machines to paint, write, compose, and play Foster, David, Karl Friston (forfatter av forord), Beijing, O'Reilly, xxvi, 426 sider, [2023]; © 2023, isbn:9781098134181,
The course description is retrieved from FS (Felles studentsystem). Version 1