Artificial Intelligence in Business and Industry
Practical information:
Want to register?
- Prerequisites: Python proficiency is required.
- Price: € 4300
Sessions are taught in English and scheduled on Tuesdays from 1 pm until 9 pm.
Georganiseerd door:
The programme has been designed with a specific need in the job market in mind: many current employees in for example R&D divisions of leading companies are highly skilled but did not receive specific training on AI in their formal education. Perhaps you, too, experience the need to bring your AI skills up to date and are looking for a qualitative AI course to do that. This postgraduate training allows professionals to acquire in one year a solid academic knowledge of artificial intelligence, as well as insight into the domains of image and language (computer vision/NLP) and business aspects of AI.
The postgraduate programme Artificial Intelligence in Business and Industry stands firmly on its own but also opens the door to more. Participants can choose to follow a follow-up track into the Advanced Master AI in Business and Industry, where they gain access to the wider range of in-depth, broadening and, in particular, more applied modules. The courses offered in the postgraduate programme form an integral part of the Advanced Master Artificial Intelligence in Business and Industry, and participants who pass the courses in the postgraduate programme will be granted exemptions when following the rest of the master’s programme.
Target audience
The programme aims at different kinds of engineers (e.g. in Engineering Science, Engineering Technology, or Business Engineers who majored in data science or applied computer science). In addition, the programme is open to masters in mathematics and physics. In general, any participant with a master’s degree that offers sufficient background in mathematics, programming and technology will be admitted. If you do not have one of the aforementioned master diplomas, you can submit a file with your motivation and CV to the programme committee, which will assess your application. Note that Python proficiency (basic) is required. An optional 6-month subscription to Datacamp can be provided upon registration.
While recently graduated students are welcome, the programme also offers added value to professionals with field experience, wishing to re- or upskill themselves in the field of Artificial Intelligence in order to make the most of their career opportunities. The programme specifically caters to participants looking for a theoretical, academically grounded approach to AI. Given the advanced content of the courses, the profiles most suitable for this postgraduate are, for example, IT developers and functional analysts or R&D staff, engineers, project leaders and managers.
Programme structure:
First semester (Sept-Dec): focus on AI foundations
- Fundamentals of Artificial Intelligence (5 ECTS)
- Machine Learning and Inductive Inference (4 ECTS)
- Artificial Neural Networks and Deep Learning (4 ECTS)
Second semester (Jan-June): focus on Business & Industry
- Computer Vision and Natural Language Processing (6 ECTS)
- Business Analytics (6 ECTS)
The programme starts with the theoretical AI foundations that are indispensable for professionals. Participants therefore get 3 academic courses that teach them the scientific basics of artificial intelligence. In addition, the door is opened to industrial applications and general business applications with the courses in the second semester.
Fundamentals of AI
In this course, you will acquire a deep knowledge and insight into foundational techniques from Artificial Intelligence, including search methods and their applications to games, the version spaces algorithm for machine learning, constraint processing techniques, strips planning and theorem proving for first-order predicate logic. You will be able to simulate each of the above techniques with pen and paper on small new examples and have insight into the relevance of these techniques for applications in domains such as manufacturing, health, education, logistics, manufacturing, and robotics.
Machine Learning and Inductive Inference
This course will familiarize you with the domain of machine learning,
which concerns techniques to build software that can learn how to
perform a certain task (or improve its performance on it) by studying
examples of how it has been accomplished previously, and in a broader
sense the discovery of knowledge from observations (inductive
inference).
After following this course, you will
- have a basic understanding of the general principles of learning
- have an overview of the existing techniques for machine learning and data mining
- understand how these techniques work, and why they work
- be able to implement programmes that learn or exhibit adaptive behavior, using these techniques
- be up-to-date with the current state of the art in machine learning research
- be able to contribute to contemporary machine learning research
Artificial Neural Networks and Deep Learning
The course aims to introduce the basic techniques, methods, and properties of ANN and study their application to selected problems. The basic concepts will be introduced in the lectures. Advanced topics and recent research results will be touched upon occasionally. You will study and develop explicit neural network models for selected applications.
Computer Vision and Natural Language Processing
The course introduces natural language processing technologies and their applications in a variety of tasks, including text mining, machine translation, question answering, and dialogue modelling. It also introduces computer vision algorithms and their applications, such as image classification, object detection, and image segmentation. Special attention goes to applications that require the joint processing of language and visual data, as this is a natural way to interact with machines.
The students will gain insights into suitable machine learning algorithms that ideally are trained with limited annotated examples or human feedback. They will learn how to build and critically assess an application making use of the most recent techniques and resources.
Business Analytics
In this course, you will learn to understand how business problems can be formulated with advanced analytics techniques as a potential solution. You will be able to reason on the organizational and managerial aspects of applying big data and analytics techniques and understand how prescriptive analytics and causal ML can help to use analytics for business decision-making. The course teaches you how analytical modelling techniques can be optimized and evaluated from a profit-driven perspective and how analytics techniques can exploit network-based information.
After following “Business Analytics”, you will know how to deal with unstructured data in the form of textual inputs, and how to use such data for practical business applications such as sentiment analysis or social media analytics. The course deals with how state-of-the-art explainability techniques can give insights into black-box machine learning models and how process mining techniques can be applied to data sets originating from process-aware information systems, including automated process discovery, conformance checking and extension. Upon completion of the course, you know which data science tools and environments are important for realizing applications of machine learning in business, including platforms such as Hadoop, Spark, etc, which business applications might benefit from deep learning techniques, and how to apply them and evaluate their appropriateness.
Teachers / speakers
Alireza Gharahighehi is a senior researcher in itec, imec research group at KU Leuven. He is mainly working on personalization and recommendation systems in media applications. His main research areas of interest are news recommendation, beyond accuracy evaluation and cold-start problem in recommender systems. Apart from his research work at itec, Alireza is also involved in the advanced master of Artificial Intelligence in Business and Industry at the KU Leuven campus Kulak in Kortrijk.
Toon Goedemé studied electrical engineering at KU Leuven. He received the Ph.D. degree in vision-based topological navigation from KU Leuven, in December 2006, under the guidance of Prof. L. Van Gool and T. Tuytelaars. Afterwards, he started teaching at the Technical University De Nayer, Sint-Katelijne-Waver, where he founded his research group Embedded and Artificially Intelligent Vision Engineering (EAVISE), in 2008. Nowadays, his group is integrated in the KU Leuven and consists of three professors (Joost Vennekens, Patrick Vandewalle, and himself), four postdocs and about 20 researchers, playing a vital role in the transfer of computer vision and AI know-how from academic research towards the industry. Since 2014, he has been an Associate Professor with KU Leuven. He is the (co)author of more than 190 international publications and was a project leader of more than 75 industrially co-founded research projects. Together with his team, he won several awards, such as the Best Paper Award at Embedded Vision Workshop CVPR 2015, the Best Demo Award at BNAIC 2015, the Best Paper Award at CGVCVIP 2016, the Willy Asselman Award for research achievements in 2016, and the Best Paper Award at Embedded Vision Workshop ECCV 2020. He is also an Associate Editor of the IET Computer Vision journal and the MDPI Journal of Imaging.
Since 2017, I have been conducting research at the LIIR group of the Human-Computer Interaction group of the KU Leuven funded by a FWO PhD Fellowship (SB) and later a FWO Postdoctoral Fellowship. This allows me to combine my passion for fashion with my interest in AI and deep learning. More precisely, my research interests are computer vision, natural language processing, recommender systems, information retrieval, multimodal representation learning, disentangled representation learning, attention mechanisms, and explainable and responsible AI.
Since 2022, I teach the lectures on natural language processing for the course Computer Vision and Natural Language Processing (B-KUL-H0Q35A) taught at the Master for AI in Business and Industry at KU Leuven Kulak.
Marie-Francine (Sien) Moens holds a MSc ("licentiaat") in Computer Science and a PhD degree in Computer Science from KU Leuven. She was appointed as a BOF-ZAP Research Professor in 2005 at KU Leuven. She is holder of the ERC Advanced Grant CALCULUS (2018-2024) granted by the European Research Council. From 2012 till 2016 she was the coordinator of the MUSE project financed by Future and Emerging Technologies (FET) - Open of the European Commission. In 2021 she was the general chair of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). In 2011 and 2012 she was appointed as chair of the European Chapter of the Association for Computational Linguistics (EACL)and was a member of the executive board of the Association for Computational Linguistics (ACL). She is currently associate editor of the journal IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and was a member of the editorial board of the journal Foundations and Trends® in Information Retrieval from 2014 till 2018. From 2010 until 2014 she was a member of the Research Council of KU Leuven and from 2014 until 2018 she was a member of the Council of the Industrial Research Fund of KU Leuven. From 2014 till 2018 she was the scientific manager of the EU COST action iV&L Net (The European Network on Integrating Vision and Language). In 2014 she was a Scottish Informatics and Computer Science Alliance (SICSA) Distinguished Visiting Fellow. She is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Felipe Kenji Nakano is a senior researcher in itec, imec research group at KU Leuven, funded by FWO. He is mainly working on structured output prediction tasks with imperfect data applied to (bio) medical problems. His main research areas of interest are (hierarchical) multi-label classification, multi-target regression, semi-supervised learning, active learning, positive-unlabeled learning, weak-label learning. Apart from his research work at itec, Felipe is also involved in the advanced master of Artificial Intelligence in Business and Industry at the KU Leuven campus Kulak in Kortrijk.
Postdoctoral researcher on process mining and machine learning at KU Leuven
Dries Vandaele is teamhoofd Documentbeheer binnen Het Facilitair Bedrijf en samen met zijn team zet hij hun collectieve kennis, ervaring en inzichten in voor het ontwikkelen en aanbieden van kwaliteitsvolle gemeenschappelijke dienstverlening aan de Vlaamse overheid, bijvoorbeeld Archiefdepot Vlaamse overheid en DigiPost, maar ook aan overheden in Vlaanderen zoals Digitaal Archief Vlaanderen, opleidingen omtrent archief en informatiebeheer …
Patrick Vandewalle received a M.Sc. degree in electrical engineering from KU Leuven, Belgium, in 2001, and a Ph.D. degree in computer and communication sciences from EPFL, Switzerland, in 2006. Until 2007, he was a post-doctoral researcher at EPFL. His research at EPFL was focused on super-resolution imaging and reproducible research. From 2007 to 2018, he was a senior research scientist at Philips Research in Eindhoven, The Netherlands. He worked on video processing algorithms for glasses-free 3D displays and high dynamic range video formats. Since 2018, he is associate professor at KU Leuven, Dept. of Electrical Engineering (ESAT), in the EAVISE research group at Campus De Nayer in Sint-Katelijne-Waver.
His main research areas are in computational imaging, image enhancement and computer vision. He currently focuses on 3D understanding and reconstruction for applications such as building 3D environments, augmented or virtual reality (AR/VR) and autonomous vehicles.
Celine Vens is professor at KU Leuven campus Kulak and leading the machine learning and AI subgroup at itec. Her main research interest is the development of machine learning algorithms for applications in health and education. She focuses on non-standard supervised and semi-supervised learning tasks, such as multi-output prediction, time-to-event prediction and interaction prediction. Besides research, she is teaching several courses at the Faculty of Medicine.
Assistant Professor at KU Leuven, Declaratieve Talen en Artificiële Intelligentie (DTAI)