General Information
| Course Code | X_401089 |
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| Credits | 3 EC |
| Period | P1 |
| Course Level | 100 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | dr. I. Tiddi |
| Examiner | dr. I. Tiddi |
| Teaching Staff |
prof. dr. K.S. Schlobach dr. A. Yaman B. Borsboom prof. dr. F.A.H. van Harmelen dr. M.C.A. Klein dr. V.N. Stebletsova dr. I. Tiddi dr. N. Ullah |
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Part of programme(s)
Bachelor
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Meeting, Seminar, Lecture |
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Course Objective
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)
Course Content
The course combines 3 types of lectures:
- general lectures to survey the richness of the area of Artificial Intelligence (AI)
- methodology lectures on how to conduct research in AI ;
- individual working groups sessions where both theory and technical skills are discussed.
In addition, students will work in groups on small projects to find, analyse and solve (theoretically) an interesting AI problem using material gathered from the lectures.
The goal of the course is to provide a broad introduction to AI and to provide students with the basic academic skills and knowledge that are necessary for studying AI at the Bachelor's level.
Additional Information Teaching Methods
- General Lectures on subfields of AI
- General Lectures on the methodology for AI research
- Working groups
Method of Assessment
Assessment is based on:
- a group assignment split over several weeks
- individual contributions consisting in online quizzes
- written MCQ exam about topics discussed in class
For students who failed, there will be an opportunity to resit the submissions (either resubmit reflections or improve the group report).
A passing grade of 5.5 is required in every assessment part.
Literature
As part of the group project, a literature search will be required on a self-chosen topic.
Additional Information Target Audience
Bachelor Artificial Intelligence (year 1)Additional Information
Use of GenAI : Level 2a (only permitted for orientation and brainstorming)
Recommended background knowledge
Student can use the following book as support :
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (https://aima.cs.berkeley.edu/)