Artificial Intelligence

This course provides a broad introduction to regression, classification, and clustering methods. It introduces the fundamentals of supervised and semi-supervised methods and their applications. The students will be familiar with the commonly used algorithms such as supported vector machine, random forests, and logistic regression. Throughout the course, the students will learn both theories and practices.  

This course provides a broad introduction to different types of data mining and required tools. The student will develop the understanding of the main stages of data mining and technologies. The course also provides the students with the general knowledge of the fundamentals of computer vision. Throughout the course, the students will learn both theories and practices.  

The course will provide insights into methods and algorithms for big data systems. The student will be familiar with different big data structures and usage of the convenient algorithms. The student will have a broad understanding of the big data systems’ design and performance trade-offs. Throughout the course, the students will learn both theories of big data systems and practices their design and implementation. 

The main aim of this course is to provide students with the fundamental knowledge of ethics and sustainability necessary for responsible innovation and the development of new information technologies (IT) in the modern society. The central topics include the role of ethics in responsible innovation and IT development; information privacy concerns, social, economic, and environmental impacts of innovations and new ITs; and how IT development and innovation can contribute to achieving the UN Sustainable Development Goals. In covering ethical and sustainability issues, the course addresses the perspectives of various stakeholders at the individual level (IT developers, innovators, consumers, investors), the organizational level (commercial, public, and non-governmental organizations), and the societal level (local and regional communities, nations, international society). Group work on possible solutions to real-life ethical and sustainability challenges constitutes an essential part of the course.

This course provides a broad introduction to advanced machine intelligence algorithms which includes unsupervised, transfer and reinforcement learning. It introduces the fundamentals and applications of convolutional networks and deep learning methods. The students will develop a broad knowledge of the foundation of interpretable and explainable methods. Throughout the course, the students will learn both theories and practices.

Throughout the course, the student will be well able to distinguish the difference between classical and modern data analysis. It also provides the students with the general knowledge of stages and fundamentals of feature analysis and algorithm performance analysis. This course provides a broad introduction to data driven decision making frameworks. The student will be able to conduct algorithm performance analysis, testing and validation. Throughout the course, the students will learn both theories and practices. 

This course is intended as a master level introduction to research methodology and the research process. This introduction gives the students an overview of the basic concept, methods, and practice of research. 

Research is a cyclical process where new and carefully planned investigations build and extend on established work. The aim is to provide students with a fundamental understanding of research as a conceptual, empirical, and practical approach to gathering new insight and knowledge. The content provides a broad overview of how researchers work within the fields of economy, innovation, and technology. It presents students with relevant methods from these domains, along with their possibilities and limitations. 

Students will learn a systematic approach to empirical investigation, including literature search, research design and methodology, qualitative and quantitative analyses, and the presentation and evaluation of results. 

At completion of the course, students will be able to study and interpret existing research on a topic and suggest approaches to broaden or deepen knowledge within a given topic.

The overall objective of this course is to help students conceptualize and prepare a timely and relevant research proposal, and to nurture a sense of inquisitiveness and active participation in research. The course aims at offering insight into the process behind a successful research project. It has an applied approach that involves collaborative and reciprocal partnerships between the university (faculty, staff, and/or students) and external communities for the mutually beneficial exchange of knowledge and resources. The research proposal forms the basis for the master thesis and the allocation of supervisor(s).