Intelligent Systems

2019-2020

Course Objective

Knowledge and understanding: at the end of the course, 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: 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)
research.

Course Content

This course will provide an in-depth understanding of classical
Artificial Intelligence problems and approaches, such as search,
knowledge representation and machine learning, by deepening the
theoretical understanding and ability to apply those techniques in
practice.

This course will also give an overview of the theory and practice of
Intelligent Systems, namely systems that perceive, reason, learn, and
act intelligently.

Students will acquire practical skills in developing intelligent systems
building up on a thorough understanding of well-understood Artificial
Intelligence approaches, including Knowledge Representation and Machine
Learning.

Teaching Methods

The course will be centered on the practical task of designing
intelligent agents that perform in a challenging, and hitherto unsolved
complex card game, against humans or other agents.

There will be 12 lectures in the first 3 weeks, as well as a number of
practical sessions in lab and working groups to help with the course
material. There is also a significant amount of self-study, both to
familiarise oneself with the AI theory and methods, and to program an
Intelligent System using those methods.

Method of Assessment

There will be a digital exam (counting for 40%) and a groups assignment
(60%) of the final grade.

There will be a resit exam, but NO resit for the group assignment.

Literature

Russell, Norvig: Artificial Intelligence: A Modern Approach. Most recent
Edition. Recommended, but not compulsory. There will be a reader.

Target Audience

BSc Artificial Intelligence (year 2)
BSc Information Sciences (year 2)
BSc Computer Science (year 2)
BSc Business Analytics (constrained choice)
BSc Biology (elective)

General Information

Course Code X_401086
Credits 6 EC
Period P3
Course Level 200
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. K.S. Schlobach
Examiner dr. K.S. Schlobach
Teaching Staff dr. K.S. Schlobach

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture, Practical
Target audiences

This course is also available as: