A close link between education, research and collaboration is the starting point for all our activities. We offer a wide range of attractive professional programs, in medicine, psychology, law and engineering. Our vision is to be a prominent university for a knowledge-driven society.
With the advances of modern technology, cybersecurity has become hard to provide and guarantee. AI can enhance cybersecurity, but also undermine it. In this course, you will learn the different uses of AI for defending and attacking a cybersystem, from fingerprint recognition for authenticating legitimate users, to fuzzing attacks for crashing vulnerable targets.
As AI systems become more common and expand their abilities, the decisions they made have a crucial impact on society as a whole. Whether they are designed to recommend content or product online, to assist judges or physicians in their decision-making, or to decide how to distribute mortgages or video surveillance cameras, these systems can have a crucial and lasting impact on all of us. For this reason, it is of paramount importance that those in charge of designing such systems work toward ethical and responsible systems. This course covers the theoretical and practical aspects of normative ethics and how they apply to AI systems, discuss how AI systems can become biased, as well as how to prevent and correct possible bias. Through concrete examples, case studies, and project, this course aims at raising awareness on the problem of ethical AI as well as giving the students practical experience on how to ensure ethical and responsible development of AI systems in their everyday work. This course is given on-line with three mandatory Zoom-meetings.
ROS (Robot Operating System) is a common set of tools used in academia to do research within autonomous systems. It shortly provides a middleware for handling communication, as well as interfacing sensors and actuators, visualization, simulation and datalogging and infrastructure where it is easy to share your own methods and algorithms. The latter has allowed a large set of different of state-of-the-art research approaches to be readily available for downloading. Due to its popularity it is also getting more widespread in the industrial community, especially in R&D. This course will give you hands-on experience how to utilize these tools and apply them to a problem of your choice.
Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.
Producing good machine learning models using the algorithms discussed in Introduction to Machine Learning – Part I typically depends on proper usage and preprocessing of the data, as well as the ability to interpret and act on the results generated by the method. Thereto, you will learn in this course more about the practical side of applying the machine learning techniques to actual problems including feature extraction methods, to figure out which part of the data is relevant to your problem, the bias-variance dilemma, to figure out if you have enough data for your model, ensemble learning, for combining more than one machine learning technique, and other experience-based practical recommendations. Armed with this knowledge, you will have to solve a classification problem in competition with the rest of the class, getting a hands-on experience in using Machine Learning.
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that studies how computers process human language. This course allows learners to obtain knowledge about NLP problems, applications, and recent methods for solving them. The aim of this course is to expertise in applying neural networks and deep learning methods to solve ongoing problems in NLP. This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) which includes 15 credits of computer programming. In addition, English B/English 6 is required. This course is given on-line with three mandatory Zoom-meetings.