Course ObjectiveKnowledge 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
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 developements 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)
Course ContentThe course will provide an introduction to some of the basic concepts of
Artificial Intelligence, such as search, adversarial search, knowledge
representation and machine learning.
Teaching Methods2 lectures of 2 hours per week.
Working groups to practice the theoretical material.
Practical groups to apply the acquired knowledge.
Method of AssessmentThe grade will be determined via a (digital) exam (100%), but there are
also 3 mandatory practical assignments (pass/fail), and all three need
to be passed.
Target AudienceBSc Artificial Intelligence (year 1)
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. K.S. Schlobach|
|Examiner||dr. K.S. Schlobach|
dr. K.S. Schlobach
dr. M. Cochez
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Seminar, Lecture, Practical|
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