General Information
| Course Code | XB_0031 |
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| Credits | 6 EC |
| Period | P2 |
| Course Level | 100 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | prof. dr. K.S. Schlobach |
| Examiner | prof. dr. K.S. Schlobach |
| Teaching Staff |
M. Beijerbacht prof. dr. K.S. Schlobach |
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Part of programme(s)
Bachelor
Premaster
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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 | Seminar, Practical, Written partial exam, Lecture |
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Target audiences
This course is also available as:
Course Objective
Knowledge and understanding: at the end of the course, the students will be familiar with basic knowledge of some of the core aspects of AI: state-space representations, search, adversarial search, logic, automated reasoning, reasoning with uncertainty and vagueness and machine learning.
Applying knowledge and understanding: students will be able to implement basic (adversarial) search algorithms, as well as knowledge based and adaptive methods to build Intelligent Agents.
Making judgements: the students will have a basic understanding of the ethical and societal implications of the developments in AI.
Communication skills: students will be able to write a scientific reports about an original research question in a group of students.
Learning skills: students will be trained in acquiring a set of complex AI related topics in a restricted period of time, come up with an original research question and perform the necessary (empirical) research.
Course Content
The course will provide an introduction to some of the basic concepts of Artificial Intelligence, such as search, adversarial search, knowledge representation and machine learning.
Additional Information Teaching Methods
2 lectures of 2 hours per week. Tutorials to get started with the programming exercises in the first weeks. Working groups to practice the theoretical material (2 hours). Practical groups to apply the acquired knowledge (flexible, on demand)
Method of Assessment
The grade will be determined via two (digital) exams (35%+35%), and a number of practical assignments (30% in total). All three components have to been completed successfully to pass the course. There will be a (single) resit exam, combining both partial results, but no resits for the practical assignments.
Literature
Chapters from "AI a Modern Approach", Russell and Norwig, provided as a part of a reader.
Additional Information Target Audience
Bachelor Artificial Intelligence (year 1)Recommended background knowledge
Some basic Programming in Python, as taught in Computational Thinking, and in parallel with Introduction to Programming course in P1.