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Fundamentals of Artificial Intelligence

Vak
2024-2025

Admission requirements

Students need to be registered for the minor AI and Society to follow this course.
This course is to be taken in parallel with the other foundational course in the minor: Philosophy, ethics and politics of AI.
Students of all faculties can register for the minor.

Description

This course provides basic notions, concepts and terminology of Artificial Intelligence (AI). The focus is on understanding the project of AI from the foundational mathematical/computational concepts and techniques. AI applications are examined both from a technological and a functional perspective. Human intelligence and artificial intelligence are compared and contrasted.

This interdisciplinary course focuses on introducing and familiarizing the students with the technological and (neuro)psychological perspectives on AI (where the companion course focuses more on the conceptual underpinnings and normative impacts). These fundamentals will help students to get a realistic understanding of AI, AI technology and the basic concepts and techniques underlying it. Students will interactively practice with reading some (pseudo)code.

Both courses Fundamentals of AI and Philosophy, Ethics and Politics of AI, in combination, aim to demystify the project of AI and give students a techno-realist conception of the current state of the art and what AI can realistically be expected to achieve.

Course objectives

Objectives of the course

  • To develop a common frame of reference and terminology for AI for the students of the minor program

  • To develop basic knowledge and understanding of the fundamentals of AI, including technical history, typology and current status of AI

  • To develop basic insight into functionality and limitations of different types of AI applications for different contexts

Achievement levels
The following achievement levels apply with regard to the course:
Upon completion of the course, students can

  • describe and explain basic concepts of AI

  • distinguish and recognize types of AI and AI techniques

  • apply basic AI terminology to classify different applications of AI in society and their own discipline

  • discuss societal impacts of AI from different perspectives using a common vocabulary

Timetable

Check MyTimetable.

Mode of instruction

Lectures/Seminars

  • Number of (2 hour) lectures/seminars: 10

  • Names of lecturers: Lecturer(s): Daan Pelt (LIACS), guest presentations/demos.

  • Required preparation by students: materials and instructions as provided via Brightspace. There will be a practical assignment roughly midway.

**Important note about the character of the sessions: **

Active preparation and participation of all students is expected in all lectures/seminars. This interaction serves to get acquainted with the diversity of backgrounds of the participating students in the minor, to optimize the delivery of the course material to the group composition, and to leverage the interdisciplinary interaction between students within the minor program.

Assessment method

Examination form(s)

  • Practical assignment: 50% of final grade

  • Written exam (entry conditional on passing the practical assignment): 50% of final grade

Areas to be tested within the exam
The examination material consists of all content covered in the lectures/seminars, the required readings as listed on Brightspace, and the slides.

Reading list

Obligatory course materials
See brightspace.

Recommended course materials
See brightspace.

Registration

Registration for courses and exams takes place via MyStudymap. If you do not have access to MyStudymap (guest students), look here (under the Law-tab) for more information on the registration procedure in your situation.

Contact

  • Coordinator: Dr. D.M. Pelt

  • Work address: Snellius, Niels Bohrweg 1, room 131

  • Contact information:

  • Telephone number: +31 71 527 4799

  • Email: d.m.pelt@liacs.leidenuniv.nl

Institution/division

  • Institute: Leiden Inst of Advanced Computer Science

  • Department: Data Science

Remarks