Language: English
Advanced course on databases
The course presents advanced topics in databases, like physical storage and indexing, query processing and optimisation, transaction processing, concurrency control and error recovery. After completing the course, the student will be able to: Student can choose to study the course online or attend the lectures. The exam is organised in Turku or Vaasa. More information
Introduction to artificial intelligence
Max amount of FITech students: 100 Persons without a valid study right to a Finnish university have preference to this course. The course considers basic methods and problem solving with artificial intelligence-based approaches. The course is concentrated around concepts of search, regression, classification and clustering. The course highlights differences between supervised and unsupervised learning. The
Digital image processing
Max amount of FITech students: 30 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. The course gives an introduction to digital image processing methods. The emphasis is in various image filtering techniques implemented both in spatial and frequency domains. Also, image compression and
Digital ethics and sustainability
Max amount of FITech students: 65 adult learners We explore the key sustainability challenges facing the environment, society, business and the information technology sector, and ask some difficult and justified questions about how to address these challenges. These issues are explored on the organisational and the broader institutional or societal levels introducing both mainstream and
Digital platform economy
Course content overlaps with course CS-E5310 ICT enabled service business and innovation (5 ECTS). Please choose only one of them. Course content Enablers of digitalisation, convergence, commoditisation, consumerisation (of IT), democratisation, disaggregation, disintermediation, power of digital aggregators and intermediators, specialisation, lowering entry barriers, servitisation, datafication, mobility, social media. Platforms, platform economy, network effects, boundary resources
ICT enabled service business and innovation
Max amount of FITech students: 15 adult learners The course will begin already in the first week of October with independent self-study modules. Lectures will begin on the 25th of October. This course is a combination combination of online material, theory lectures, visiting lectures from industry, and case assignments from the following themes: Learning outcomes
Social media analytics
Max amount of FITech students: 100 This course introduces social media as a data source and platform for understanding, exploring, and solving various societal issues. The relevant theoretical concepts around social media, analytic tools, and affordances of different social media platforms such as Twitter, Facebook, Instagram and YouTube will be discussed in detail. The students
Introduction to digital transformation
Max amount of FITech students: 65 adult learners This course aims to demystify digital transformation and view it from a number of different perspectives such as general, technology, society level and company level perspectives. The videos include an informal discussion with experts who have extensive experience from the area both from an academic perspective as
Introduction to IoT-based systems
The course introduces Internet of Things (IoT) systems as well as basics of concepts that are necessary to understand IoT systems. Course contents Learning outcomes After the course, the student will be able to These skills are needed to built IoT systems for practical industrial applications. Teaching methods Weekly video lectures that are available online.
Machine learning
Max amount of FITech students: 60 adult learners The main concepts as well as the different types of machine learning are covered on this course. The approach of this course is to cover machine learning from algorithmic point of view. The aim of this approach is to understand the theories/algorithms behind machine learning algorithms and