Max amount of FITech students: 40
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 how to select the best one for our specific problem, to know their limits, and even how to modify it to fit our specific problem.
This course is highly useful wherever there is data to be analysed. Hence, the application area is huge either in industry, factories, power plants, social science, business, finance, etc.
After completing the course, the student will be
- able to explain the manifestation of machine learning and their possible applications. Furthermore, they will be familiar with several concepts like data modelling, overfitting, underfitting, generalisation, memorisation, learning data, and validating data.
- aware of supervised learning algorithms and their different kinds and applications.
- able to apply different regression methods as well as neural networks to capture hidden relations in supervised learning.
- able to explain probabilistic models and Bayesian based machine learning algorithms.
- aware of data quality in machine learning and how to improve and clear data.
- able to explain classification algorithms as well as apply them in simple scenarios.
- aware of unsupervised learning concepts and clustering.
- able to define reinforcement learning and its main differences with supervised and unsupervised machine learning.
- aware of the applications as well as limitations of machine learning algorithms.
It is possible to complete the course online, but it has a fixed schedule. The course consists of quizzes, exam and simulation projects.
More information in the University of Vaasa study guide.
You can get a digital badge after completing this course.