Master of Artificial Intelligence (Leuven)

The AI program aims at instructing and training students on state of the art knowledge and techniques in Artificial Intelligence, with specific focus either on Engineering and Computer Science (ECS), on Speech and Language Technology (SLT) or on Big Data Analytics (BDA), depending on the selected option within the program. It aims at introducing the students to the concepts, methods and tools in the field.

It aims at instructing students on the achievements in a number of advanced application areas and make them familiar with their current research directions. It aims to bring students to a level of knowledge, understanding, skills and experience that are needed to actively conduct basic or applied research on an international level. In particular, it aims to provide students with a critical scientific attitude towards the central themes of A.I.

As a master-after-master program, it is assumed that the students entering this program have already achieved the general skills and attitudes defined for any master program. Nevertheless, it is also within the aims of the program to further strengthen the skills and attitudes, within the specific scientific context that AI offers.


ECS-option: In the ECS option, in addition to the above, the program aims at instilling a problem-solving attitude towards the practice of AI. Upon completion of the program, students should be familiar with the fundamentals of AI, be aware of its reasonable expectations, have practical experience in solving AI-problems and be acquainted with a number of advanced areas within the field.


SLT-option: In the SLT-option, in addition to the general aims, the program aims to provide all necessary background and skills which are required to fully understand and to actively participate in the fast developing multi-disciplinary field of Language and Speech. This includes a thorough understanding of the theories and models that shape the field, as well as practical experience with a variety of technologies that are used and currently developed.


BDA-option: In the BDA-option, in addition to the general aims, the program aims for the same additional goals as the ECS-option, but specialized to Big Data Analytics. In particular, it aims at instilling a problem-solving attitude towards the practice of Big Data Analytics. Upon completion of the program, students should be familiar with the fundamentals of Big Data Analytics, be aware of its reasonable expectations, have practical experience in solving BDA-problems and be acquainted with a number of advanced areas within the AI-subfield of BDA.


General end terms for the program as a whole.

a. Knowledge level:
Successful students should be able to understand the concepts, the methods, and the applicability of the fundamentals of AI, including:
- knowledge representation formalisms,
- search and problem solving techniques,
- basics of machine learning, constraint processing and planning,
- at least one broadening theme in AI: either in Cognitive Science or in Philosophy of Mind and AI or in Privacy issues in AI.
Successful students should be familiar with the concepts and techniques of an Object Oriented programming language and either of an AI-programming language or of specific issues required for Big Data programming.
Successful students should be familiar with the basics of several advanced areas of AI and with the current research directions taken in these areas.

b. Skills:
General:
Successful students should be able to formulate research goals, determine trajectories that achieve these goals, collect and select information relevant to achieve the research goals and interpret collected information on the basis of a critical research attitude.
They should be able to read and comprehend the international scientific literature on AI (in English).
They should be able to write a scientific paper on AI (in English).
Specific:
Successful students should be able to write small-scale programs in an Object Oriented programming language and in either an AI-programming language or in the context of Big Data programming.

c. Attitudes:
Successful students should possess an attitude of approaching and investigating AI and AI-problems from a multi-disciplinary perspective.


Additional end terms specific for the ECS option.

a. Knowledge level:
Successful students should be familiar with the more advanced issues in AI, including:
- logic for representation and problem solving,
- neural networks, their basic techniques and applications,
- machine learning techniques,
- the treatment of uncertainty in knowledge systems,
Successful students should be familiar with an AI-programming language.
Successful students should be familiar with the basics of several advanced methodologies and/or application areas of AI and with the current research directions taken in these areas.

b. Skills:
Successful students should be able to
- apply AI techniques and tools in the development of an AI-application,
- develop a small-scale AI-system,
- write small-scale programs in an AI-programming language,
- critically compare, relate and evaluate the relative merits of different approaches to certain classes of AI-applications,
- perform research in one of the research areas of Artificial Intelligence.
They should be able to solve problems using these fundamentals of AI i.e. be able to extract an AI problem from a real world situation, resolve the problem using AI techniques, evaluate the solution method and test the solution.


Additional end terms specific for the SLT option.

a. Knowledge level:
Successful students should have a solid background in
- linguistics
- speech science
- natural language processing
- speech signal processing
- pattern recognition

b. Skills:
Successful students should have experience with the technological and scientific activities performed in companies or research centres in the speech and language technology area.
Successful students should be able to
- critically compare, relate and evaluate the relative merits of scientific techniques used in companies or research centres in speech and language technology,
- actively participate in the research activities of such centres.


Additional end terms specific for the BDA option.

a. Knowledge level:
Successful students should be familiar with the more advanced issues in AI, including:
- optimization in constraint processing and local search,
- data and statistical modelling,
- machine learning techniques,
- data mining techniques.
Successful students should be familiar with issues involving programming for big data.
Successful students should be familiar with the basics of several advanced methodologies and/or application areas of Big Data Analysis and with the current research directions taken in these areas.

b. Skills:
Successful students should be able to
- apply AI techniques and tools in the development of an BDA-application,
- develop a small-scale BDA-system,
- write small-scale programs for programming with big data,
- critically compare, relate and evaluate the relative merits of different approaches to certain classes of BDA-applications,
- perform research in one of the research areas of Big Data Analytics.
They should be able to solve problems using these fundamentals of BDA, i.e. be able to extract an BDA problem from a real world situation, resolve the problem using BDA techniques, evaluate the solution method and test the solution.