Artificial Intelligence and Machine Learning

15 credits

Syllabus, Master's level, 2IS074

Education cycle
Second cycle
Main field(s) of study and in-depth level
Information Systems A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Department Board, 26 April 2018
Responsible department
Department of Informatics and Media

Entry requirements

90 credits in information systems or the equivalent

Learning outcomes

In terms of knowledge and understanding, after completed course the student should be able to:

- describe central machine learning methods and techniques and how they relate to artificial intelligence,

- account for the basic principles of the symbolic and connectionist paradigm of artificial intelligence.

- understand problem-solving through search, knowledge representation and reasoning.

- be able to account for the historical background to the subject artificial intelligence, and its development and connection to other subject areas,

- explain ethical aspects of artificial intelligence and machine learning.

In terms of skills and abilities, after completed course the student should be able to:

- carry out formalisation and programming of a number of basic problem solving methods, knowledge presentation forms and types of automated reasoning,

- appy quantitative research methods.

In terms of judgement and approach, after completed course the student should be able to:

- evaluate the applicability of basic AI techniques in different knowledge domains,

- critically evaluate the ethical implications of artificial intelligence and machine learning from the perspective of individuals, organisations, and society.


This course introduces the student to the field of artificial intelligence, both according to the symbolic and the connectionist paradigms. The course begins by introducing and explaining the concepts of intelligence, knowledge, and learning. Then, the symbolic (classical) AI paradigm is covered, including state space search, heauristic search, knowledge representation, resolution, constraint satisfaction problems, meta-logic programming, meta-interpreters, and inductive logic programming. Then, the connectionist paradigm, primarily artificial neural networks (ANNs), and related topics such as deep learning and reinforcement learning, are covered. Finally, the applicability of the paradigms in different domains is discussed, as well as how can they complement each other.

The objective of the course is to teach both traditional symbolic AI and the complementary neural network based methods for the emerging area of autonomous self-learning information systems and acquisition of knowledge from big data. This course builds on fundamental techniques in statistics, formal logic and programming to enable students to master the methodologies and develop novel systems at the leading edge of machine learning technology. Ethical implications and considerations relating to artificial intelligence and machine learning are also dealt with.


Lectures, lessons, seminars, laboratory sessions.


Assignments, seminars, laboratory sessions, and written exam. Compulsory attendance is required for some elements.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.