Student Type: Degree student

  • Introduktion till informationsteknologi

    Kursinnehåll Digitaliseringen påverkar alla samhällsområden genom att förändra strukturer, processer och arbetssätt. Den här kursen ger en inblick i informationsteknologi och dess betydelse för digitaliseringen. Under kursen får du både teoretisk och praktisk insikt i grunderna i datavetenskap och datateknik, genom teman såsom datalogiskt tänkande, algoritmer, mjuk- och hårdvara, internet, AI och data. En utökad

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  • 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

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  • 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.

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  • Artificial intelligence

    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. This course presents the student overview of some of the basic AI theories and applications with practicality in mind. In the course projects, students get some experience in programming

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  • Machine learning

    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. Machine learning principles are described in lectures and practical hands-on programming tasks are done on the online Matlab platform. Course contents Learning outcomes After completing the course, student Course

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  • Speech recognition

    Max amount of FITech students: 20 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. Course contents After completing the course you will have become familiar with speech recognition methods and applications. Additionally, you will have learned to understand the structure of a typical

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  • Deep learning

    Max amount of FITech students: 30 Please note the early application deadline. This course provides an elementary hands-on introduction to deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning and obtain empirical experience on training deep neural networks. Applications of deep learning to typical computer

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  • Machine learning: Supervised methods

    Max amount of FITech students: 40 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. Mastering the prerequisite skills is very important in order to complete this course. Please list your preliminary knowledge in your application. Course contents Learning outcomes After the course, the

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  • Computer vision

    Max amount of FITech students: 50 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. The course gives an overview of algorithms, models and methods which are used in automatic analysis of visual data. Course contents Learning outcomes After the course, the student Course

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  • Speech processing

    Max amount of FITech students: 10 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. Course contents After the course, the student is able to describe and make use of basic phenomena in speech communication, and describe and apply common speech processing methods, especially

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