SIFI2300 ¨C Artificial Intelligence and Digitalization

Course content

How we live and work has been changed by current AI technologies, especially those driven by machine learning (ML). For example, generative AI (GenAI) models (like ChatGPT) have been widely discussed as technologies transforming many areas such as business, society, and daily life. This course provides a broad overview of how modern AI technologies, such as ML frameworks (including deep learning), work at a conceptual level, why they are pervasive, and how they affect individuals and society. To explore how these new technologies support such a digital transformation across many areas, we discuss common algorithms and frameworks for the development of ML models; models that adapt their behavior based on observable patterns in their training data, such as vast quantities of written text or digital images.?

The course engages students in understanding applications and prospects of AI and ML technologies, all without requiring an extensive background in programming or mathematics. Participants will also learn to critically reflect on AI and ML's potential and limitations and explore the ethical, social, and environmental implications of this technology. Participants will also gain insights into how AI and ML concepts can be applied to their specific fields of study. They learn to identify how these technologies can address unique challenges in their own fields of study successfully and explore what new challenges they might create.

Learning outcome

After completing this course, you can:

• Define artificial intelligence (AI) and its key concepts, understand its historical evolution, and explain its role in digital transformation.

• Gain insight into the general ideas behind AI and machine learning (ML) technologies.?

• Explain basic ideas and concepts in ML approaches (including supervised, unsupervised, and reinforcement learning) and know how some common ML algorithms are used in different applications.

• Understand a general idea of neural networks and deep learning and their role in the pervasive use of AI, without focusing on technical details.

• Gain a general understanding of generative AI models, such as large language models (LLMs), their capabilities, applications, and some inherent limitations.

• Explain the transformative role of generative AI in reshaping businesses, creativity, society, and daily life.

• Gain insights into how AI and ML concepts can be applied to your specific fields of study, identify potential ways these technologies can address unique challenges in your field or improve your workflows, and critically reflect on potential risks and new challenges they might introduce to your field and discipline.

• Identify and critically analyze ethical challenges, biases, societal impacts, and environmental considerations associated with AI deployment and usage.

Admission to the course

Students admitted to the?Digitalization certificate are guaranteed admission to this course. Other students will have to wait until the secondary admission periode (etteranmeldingen) and apply then.

Formal prerequisite knowledge

You must currently be enrolled in a bachelor's programme, be an incoming exchange student at the bachelor's level or be enrolled in integrated 5-year master's programmes or 6-year professional programmes at the University of Oslo (UiO).

Students must have completed at least 60 ECTS at bachelor's level (one year of full-time studies) at the University of Oslo before applying for admission to the course.

Recommended previous knowledge

You ought to have completed SIFI2000 ¨C Foundations of Digitalization prior to taking this course.

Teaching

2 hours of lectures and 2 hours of exercises each week.

There is one mandatory assignment for the course, which must be completed.

Examination

Oral exam.

The mandatory assignment must be approved in order to take the exam.

Grading scale

Grades are awarded on a pass/fail scale. Read more about?the grading system.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.