Introduction to Artificial Intelligence
Overview of AI: concepts, history, applications, and main subfields including machine learning, knowledge representation, and intelligent systems.
Tutor groups (workgroups) are mandatory — you must attend to pass the course. You can miss at most 2 sessions. Sign up for a group on Canvas immediately; the deadline can close on the first day of teaching.
Learning objectives
After completing this course, students will have: a basic understanding of a number of subfields of AI; (Knowledge and understanding) a good understanding of the academic skills required in studying AI; (Applying knowledge and understanding) some basic skills in writing a project proposal (Communication) a solid understanding of how the degree program in AI is structured. (Lifelong learning skills) some experience with reflecting on the role of AI in society (Making judgements)
The course combines general lectures surveying the richness of Artificial Intelligence (AI), methodology lectures on conducting AI research, and individual working group sessions where academic skills and AI topics are discussed. Guest lectures cover machine learning, knowledge representation, hybrid intelligence, ethics & philosophy of mind, embodied intelligence, verification & validation, and data wrangling. Students work in groups on a hypothetical AI project: choosing a problem, conducting a literature review, proposing an AI-based solution, and presenting it as both a report and a poster. The goal is to provide a broad introduction to AI and basic academic skills for studying AI at the Bachelor's level.
Assessment
Assessment is based on: a group project including a final report (~12 pages) and poster presentation (approximately 70%), and a written MCQ exam based on lecture content (approximately 30%). Weekly assignments (literature review, project proposal sections) all contribute to the final group report. For students who failed, there is an opportunity to resit by resubmitting reflections or improving the group report.
Teaching methods
General Lectures on subfields of AI (2x per week); Guest lectures from researchers in specific AI areas; Working groups (weekly mandatory tutor sessions for project work); 1 session on how to deal with diversity in the classroom