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ARTIFICIAL INTELLIGENCE
One of the most fascinating research issues today is the investigation of the true nature of ‘intelligence’, which involves the study of cognitive processes and models, natural language and perception, human knowledge representation and reasoning.
Related to these issues, possibly the ultimate research objective in science is the development of an intel-ligent agent: a robot that can perceive and communicate, through natural language as well as through vision, sensors and movement. A robot that can represent its own knowledge and reason on the basis of it, that can plan and act, that can assimilate new knowledge from experience and interaction with its environment, and, in general, that can perform any task that we tend to consider typical of intelligent living beings.
WHY STUDY ARTIFICIAL INTELLIGENCE?
Artificial Intelligence is quickly emerging from the laboratory and is venturing into the commercial marketplace. Its impact on society is growing rapidly: in speech and language technology, strategic planning and diagnosis, process and system control, vision and authentification systems, information retrieval and data mining and many other contexts. AI research is increasingly being supported by governments and industry. The many new realizations continually redefine which applications we can achieve and push existing technology to its limits. Reasoning with knowledge is a central issue. The mere fact that knowledge is power makes the importance of AI indisputable.
However, due to this rapidly expanding role of AI in our current and future society, there is an urgent need for academically trained people who are familiar with the fundamentals of AI, aware of its reasonable expectations, and have practical experience in solving AI problems. Specifically people with a variety of backgrounds, but well-instructed in AI-technology, are needed to cope with the large variety of application domains that AI is currently addressing.
THE MASTER’S PROGRAMME
The University of Leuven’s Master in Artificial Intelligence programme builds on these fascinating challenges. For more than 25 years, it has provided an internationally renowned, post-graduate study programme in Artificial Intelligence. The multidisciplinary programme trains university graduates from a variety of backgrounds, including engineering, sciences, economics, psychology and linguistics, in all areas of knowledge-based technology and its ap-plications.
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In this brochure we present the main themes and objectives of the programme. For more information on the pro-gramme, detailed course descriptions and information on lecturers, please see the following web page:
http://www.mai.kuleuven.be
ENGINEERING AND COMPUTER SCIENCE OPTION
This option is meant for students with a background in the field of engineering or science that have experience in computing concepts and practice. The option trains students in AI research and development by introducing them to AI concepts and tools, instructing them on a number of advanced application areas, and instilling a problem-solving attitude towards the practice of Artificial Intelligence.
The main topics of focus in the Engineering and Computer Science option include advanced programming languages, knowledge representation, artificial neural networks, robotics, computer vision, machine learning, data mining, biometrics, multi-agent systems, genetic algorithms, bio-informatics and support vector machines. Below, we highlight the essence of some of these themes.
UncERTAInTy In ARTIFIcIAL InTELLIgEncE
The world is inherently uncertain and machines must reason about this uncertainty in order to achieve intelligent behavior. Accounting for uncertainty is crucial for tasks such as medi-cal diagnosis, weather prediction, and estimating the position of a car from gPS-signals. The domain of Uncertainty in AI has contributed many techniques for modeling and reasoning about uncertainty. Probabilistic graphical models, which com-bine graphs with probability theory, are one of the foremost techniques. They are used in robotics, computer vision, natu-ral language processing, bioinformatics, and medical diagno-sis. They are principled and there exist effective algorithms for performing inference.
cOMPUTER VISIOn
computer vision deals with the computational processing of images and movies to assist or automate visual perception tasks. It draws on theoretical aspects of AI in order to relate images to abstract models of the world - inverse computer graphics, as it were. As computational power increases, it is possible to solve increasingly challenging problems. Success-ful examples include visual quality control in manufacturing systems, automatic surveillance, optical character recognition, remote sensing, content-based image retrieval and image-based medical diagnosis support systems.
ARTIFIcIAL nEURAL nETWORKS
Artificial neural networks attempt to capture and model the impressive computational capabilities of the human brain. By adapting the structure and weights of their architecture, they are able to learn from examples and to extract information autonomously. As a consequence, their generalization ability for organizing completely new data becomes manifest. Many of the results concerning this learning and generalization behavior are rooted in a firm mathematical basis. They have also given rise to neural network technology that is built upon efficient software development and special chip design. It has not only become part of modern industrial applications but also hides in tools that touch upon everyday life such as optical character recognition, natural speech synthesizers, car and aircraft controllers, medical diagnosis, stock market prediction and web-based data mining facilities.
ROBOTIcS
Robotics is the battlefield (or playground, if you like) of AI as it provides the ultimate test for all AI algorithms in planning, sensing, and control. Robotics is an area that unites people with vastly different expertises in order to work towards a common goal. It is also where engineers and scientists experience the gap that still exists between dream and reality in AI: human beings are still, in many respects, orders of magnitude more intelligent than any computer program. But it is up to us - and you - to take the challenge and close the gap! Try to link an expert system to a walking robot that has limited sensing and motion capabilities. Help us develop a robot that uses its vision and touch sensors to finds its work-pieces autonomously. Make our autonomous wheelchair more user-friendly and intelligent. Instruct a car recycling robot on how to talk to its operator and to explain what it thinks it is doing. Teach our robotic ant how to find its way around the lab.
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Peter Soetens previously graduated as Industrial Engineer in Electro- Mechanics. After that, he studied in the Computer Science and Engineering option of the MAI pro-gramme.
Peter: “The field of artificial intelligence had always drawn my attention in newspapers and magazines. The MAI programme seemed a good opportunity to get myself up-to-date with the fundamentals and the latest evolutions in this research area. I could choose from a broad range of courses and found a composition that met my specific interests. The programme opened a lot of new opportunities for me and many of my fellow stu-dents. I also found it interesting to act as a student rep-resentative in the educational board, which guides the long term vision of the programme, evaluates courses and aims for high quality education. My MAI degree was the key to start a PhD at the robotics lab of the university. This lab is closely involved with the pro-gramme, allowing students to get in touch with sensing, moving and reasoning with real or simulated robots.”
SPEECH AND LANGUAGE TECHNOLOGY OPTION
A key property of human intelligence is its linguistic bias. To a very large extent, we rely on natural language for acquir-ing, ordering and communicating knowledge. The sophistication and flexibility of these languages is one of the features that sets us apart from both other species on this planet and machines.
The main fields of study in the Speech and Language Technology option are understanding natural languages better and us-ing them to efficiently interact with a computer. This option of the Master of AI provides the necessary background and skills that are required to fully understand and actively participate in the rapid developments which characterize this field. The programme covers both written and spoken language, and ranges from theoretical background about the main processing techniques to concrete applications.
A characteristic of the Speech and Language option is that it also allows for an internship in a company with R&D activities in the field of language or speech processing. This provides an excellent opportunity to get some hands- on experience with real-world applications and to get a taste of what a job in this fast growing industry is like.
nATURAL LAngUAgE UnDERSTAnDIng
During the past decades, natural language understanding has improved significantly due to advanced algorithms in machine learning, optimization and reasoning. natural language un-derstanding has evolved from shallow semantic processing to a deeper understanding of the natural language utterances. This development has led to the emergence of famous ap-plications, such as question answering (e.g. IBM Watson), machine reading, understanding natural language instruc-tions and translating written stories into animainstruc-tions in a vir-tual world. There are still many research questions to resolve, such as dealing with limited and sparse training data and fus-ing world, domain and contextual knowledge into the machine understanding processes.
SPOKEn LAngUAgE PROcESSIng
The challenge of processing spoken language is to establish a link between the continuous speech signal and the discrete, symbolic representation of written language. The speech signal carries a wide variety of infor-mation about the speaker, such as speaking style, intonation, recording acoustics, and so on, which is not present in its written counterpart. In other words, we can say the same thing in amazingly different ways, while not posing any problem to a human listener. Dealing with all this variability is one of the great challenges of speech recognition; adding the extra richness to the signal is the complementary challenge for text-to-speech.
MAcHInE TRAnSLATIOn
Long before the first computers were built, people dreamt of machines that were able to automati-cally translate language. Although translation tools are now available and used by millions of people every day (google Translate, Bing Translator), many issues remain unresolved and fully automa-tic, high-quality translation of unrestricted text remains a distant dream. Machine translation com-bines techniques from different branches of language technology, such as automatic alignment, language modeling, cross-lingual information retrieval, terminology extraction from comparable corpora, syntactic and semantic parsing, natural language understanding, and natural language generation. current challenges lie in improving the models for low resource languages (translation between smaller languages), speech translation (multilingual skype), and improving the producti-vity and consistency of human translators in computer-Aided Translation tools.
Kim: “When I studied MAI, Speech and Language, everything was well organised and the courses were taught by devoted professors. They paid a lot of attention to make their lectures as illustrative, interesting and interactive as possible. The programme was a mix of ‘ex cathedra’ lectures and interactive courses, where speakers from companies were invited. A major plus factor is the internship, during which we were involved in the daily life and business of a company related to speech and language. The programme did not limit itself to the Belgian border. An international Summer School was organised, in which information could be exchanged with foreign students in the same domain. The programme was an ideal continuation
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BIG DATA ANALYTICS OPTION
Following the success of major multinational companies (Google, Amazon and many others), industry is seeking specialists that are able to analyze the massive datasets that companies collect. The goal is to extract the knowledge hidden in these large data collections in order to provide novel and interesting insights into a company’s business. This new option optimally prepares you to be the expert on data analysis that these companies desperately seek.
The option will provide instruction on the necessary skills such as programming for big data, statistical analysis, ma-chine learning, and data mining that you need to successfully fulfill these tasks. It will also make you an expert in many of the application areas, such as information retrieval, bioinformatics and image analysis, that characterize big data. Big Data is BIG and offers a great future within Computing.
THE MASTER’S THESIS
The most important part of the programme is the master’s thesis. In this study component, students conduct their own research in Artificial Intelligence. Over 15 top research units of the university offer challenging research themes to the students. From the proposed topics, every student selects one that matches his or her background and interests. Some topics are related to fundamental re-search issues in AI while others are practical experimentations and evaluations of new techniques. Students are also allowed to suggest their own research di-rection. Some topics are in collaboration with industrial partners. In some cas-es, an internship in a company can be combined with the master’s thesis. guided by researchers, students are able to define their own research trajectories and achieve new research results in challenging domains.
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MAcHInE LEARnIng
Machine learning’s aim is to design systems whose perfor-mance on a task improves with experience. currently, ma-chine learning plays a central role in many applications that pervade everyday, ranging from Web search to product rec-ommendation systems for online resellers. This programme will help provide you with insight into the inner workings of the underlying technologies that drive these systems.
With the emerging trend of big data, the importance of ma-chine learning will continue to grow and individuals with knowl-edge in this area are, and will continue to be, in high demand.
TEXT-BASED InFORMATIOn RETRIEVAL
Our society is flooded with all kinds of docu-ments. Retrieval systems and search engines help businesses, governments, institutions and individuals find relevant information in an ef-ficient and reliable way. Search engines have a particularly difficult task when they are confronted with large and heterogeneous text collections, which is the case on the World Wide Web. Text-based information retrieval studies a range of subjects, including understanding of an informa-tion need, filtering, indexing and categorizainforma-tion, and retrieval models that match query and text representations, possibly across different lan-guages. Summarization, information extraction and question-answering are text mining applica-tions that currently receive a lot of attention. Many of the applications need artificial intelligence techniques such as machine learning and pattern classification, reasoning with symbolic know-ledge and reasoning with uncertainty.COURSE SELECTION AND MASTER’S THESIS
Students with mixed interests are invited to propose motivated course selections that combine courses from the different proposed options. The programme advisory committee will evaluate these requests on the basis of their motivation and acceptable coherence. Students are required to complete a master’s thesis on a topic of their choice.
DATA MInIng
Today it is possible to collect and store massive amounts of data from a wide range of domains such as transaction data for retailers, online ratings and reviews for products, strea-ming financial transaction data, sport match statistics and text sources such as Wikipedia. The goal of data mining is automatically analyze these massive data sources in order to extract interesting and novel patterns and models from them. Data mining techniques combine ideas from fields such as databases, machine learning, statistics, and many more. no-table data mining successes include product recommendation systems, fraud detection systems, sports analytics, and Web advertising among others.
WHy STUDy In LEUVEn?
Leuven is a very lively and pleasant, small university town. It is located in the center of Western Europe, only 25 kilometers from Brussels and its 42,000 students form more than half of the population in the center of the town. So in Leuven, students do not disappear in the crowd: they are the city’s prime citizens. Social and cultural activities target the student audience. They are of-ten in English, to accommodate the many non-Bel-gian students, and are scheduled throughout the entire academic year. Moreover, movies are always shown in their original language version. Very appro-priately, the publicity slogan used by the town officials is ‘Leuven: centuries old and sparkling young’. Impressive and beautifully restored historic buildings, mixed with many attractive cafes, snack bars and restaurants, give the city a playful charm. A sparkling multicultural life adds young and dynamic features to the old town.
THE UnIVERSITy OF LEUVEn
The University of Leuven (1425) is among the oldest universities in Western Europe. Throughout its history, many eminent scholars, such as Erasmus, Mercator and Vesalius, conducted their research under the auspices of the university. Today, the university is the largest in Belgium and is highly esteemed for its view on education, firmly rooted in world leading research.
LIVIng cOnDITIOnS
Adequate student accommodation is easily found either in private homes or in university guest houses. The monthly rent (including electricity and gas) of a student room ranges from 250 to 350 Euro and a flat costs up to 450 Euro. The cost of living for a single person for one month, excluding lodging, is between 250 and 400 Euro. Personal computers can be rented cheaply from the university and access to Internet is widely available in student housing.
ADMISSIOn REQUIREMEnTS
you have successfully completed a four-year bachelor’s or master’s programme. A good knowledge of the English language is required, such as a paper-based TOEFL score of at least 550, or the equivalent. Admission is granted on the basis of your application, which must contain precise information on your academic results, mo-tivation, language skills and experience with computers and/or programming languages. Only applicants with good qualifications are admitted to the programme.
PRAcTIcAL InFORMATIOn
Type of programme:
Master’s or advanced academic programme.
Duration of programme:
One year on a full-time and two years on a part-time basis
Application:
The electronic application form can be found on http://www.kuleuven.be/application
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SECRETARIAT MASTER OF ARTIFICIAL INTELLIGENCE Department of Computer Sciencecelestijnenlaan 200A box 2402 3001 HEVERLEE, Belgium tel. + 32 16 32 78 17 [email protected] www.mai.kuleuven.be
