Student Type: Adult learner
Collective intelligence and agent technology
Max amount of FITech students: 20 Persons without a valid study right to a Finnish university have preference to this course. The course concerns the so called autonomic (“self-managed”) approach to AI, when AI is represented by autonomous intelligent agents (i.e., software robots) capable to fully manage themselves (having self-trained models of own objectives, beliefs,…
Data science for the Internet of Things
Max amount of FITech students: 15 Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course. The course covers the fundamentals of developing data science processing pipelines for data produced by Internet of Things devices. The Internet of Things (IoT) is an extension of Internet…
Cloud and edge computing
Max amount of FITech students: 10 Course contents In this course, we will look at modern cloud computing environments. We start by studying how data centres are constructed, paying particular attention to data center networking. Building on this, we define cloud computing and investigate how computation can be scaled in cloud environments. We extend the…
Value network design for internet services
Max amount of FITech students: 10 Course contents Learning outcomes The objective is to improve the student’s understanding about theory and design processes of value networks in Internet and to apply this understanding in design cases on the field. The emphasis of the course is in the field exercises implemented as team work and in…
Tietoliikenteen siirtomenetelmät
Maksimimäärä FITech-opiskelijoita: 20 Tämä kurssi käsittelee digitaalisen tiedonsiirtojärjestelmän fyysisen kerroksen perustoiminnot ja signaalin siirtoon vaikuttavat asiat. Kurssin suorittamisen edellytyksenä on todennäköisyys- ja tilastotieteen perusteiden hallinta sekä perustiedot signaalien käsittelystä. Kurssin sisältö Osaamistavoitteet Suoritettuaan tämän kurssin opiskelija Kurssimateriaali Luentokalvot (online) B. P. Lathi & Z. Ding: Modern Digital and Analog Communication Systems, International 4th ed. Opetuksen…
SDN fundamentals & techniques
Course contents This course covers the principles of legacy routing and Software-Defined Networking (SDN), an important cornerstone of the 5G and beyond network systems. The course envelops theoretical and practical aspects of SDN, showcasing the different existing SDN protocols and controllers, with focus on ONOS and OpenFlow as SDN controller and SDN protocol, respectively. The…
Performance analysis
This course teaches performance analysis of modern computer and communication systems. Course contents Learning outcomes After the course, the student Course material Lecture slides (online). Teaching schedule Completion methods Examination (100 %), exercises. More information in the Aalto University study guide. You can get a digital badge after completing this course.
Microservice architectures and serverless computing
Max amount of FITech students: 35 The introduction of cloud computing and the explosive growth of mobile computing and new online services have brought new ways of structuring computing systems: microservice architectures and serverless computing. This course is about microservice and serverless system design, not implementation. This course will not teach how to program or…
Machine type communications for internet of things
Course contents The course discusses IoT connectivity requirements such as accuracy of the time synchronicity, link reliability, maximum latency, amount of data, duty cycle, number of connecting devices, coverage as well as the technologies available to meet the requirement. Also practical device side implementation and software development aspects will be covered. Learning outcomes After the…
Machine learning for mobile and pervasive systems
Max amount of FITech students: 25 During this course, students will learn good practices for machine learning with noisy and inaccurate data. Course contents Feature extraction/feature subset selection, handling high dimensional data, ANN + deep learning, probabilistic graphical models, topic models as well as unsupervised learning and clustering, anomaly detection and recommender systems. Course material…