Postgraduate Certificate: Artificial Intelligence in Business and Industry
The postgraduate programme on AI in Business and Industry aims to give engineers, computer scientists and other professionals the opportunity to specialize in the field of artificial intelligence. This programme allows professionals to acquire in one year a solid academic knowledge of AI, as well as insight into the domains of image and language (computer vision/NLP) and business aspects of AI.
- Start of the programme: 26 September 2023
- End of the programme: 29 June 2024
- Timing of the lectures: Tuesdays from 1 until 9pm
- Location(s): KU Leuven Campus Kulak Kortrijk
- Evaluation format: As defined in the KU Leuven programme guide
- Number of credits: 25
- Contact hours: 182
- Tuition fee: 3800 euro
Purpose of the programme
Artificial Intelligence has already become an integral part of our society, influencing more and more aspects of our lives. Nevertheless, scientists believe that the AI revolution is just getting started, and that it will eventually leave an even deeper impression on humanity than, e.g., the Industrial Revolution. For example, AI has the potential to improve healthcare, make agriculture more efficient, optimise production systems or increase the safety of citizens. Companies, hospitals and other organisations want to employ AI, but often lack properly trained staff. The new Postgraduate Certificate: Artificial Intelligence in Business and Industry aims to give engineers, computer scientists and other professionals the opportunity to specialise in this field.
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 & admission requirement
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, who will assess your application.
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.
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 to general business applications with the courses in the second semester.
First semester (Sept-Dec): focus on AI foundations
Second semester (Jan-June): focus on Business & Industry
- Fundamentals of Artificial Intelligence (5 ECTS)
- Machine Learning and Inductive Inference (4 ECTS)
- Artificial Neural Networks and Deep Learning (4 ECTS)
- Computer Vision and Natural Language Processing (6 ECTS)
- Business Analytics (6 ECTS)
Programme components in detail
“Fundamentals of AI”
In this course, you will acquire a deep knowledge and insight in 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, 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).
“Artificial Neural Networks and Deep Learning”
The aim of the course is to introduce the basic techniques, methods and properties of ANN and to 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, which include text mining, machine translation, question answering and dialogue modelling. It also introduces computer vision algorithms and their applications such as image classification, object detection, 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 in 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.
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 form 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 originated from a 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, and which business applications might benefit from deep learning techniques, and how to apply them and evaluate their appropriateness.
With the support of VAIA (Flemish AI Academy)
Want to know more?
Contact person: Benedicte Seynhaeve (email@example.com)