Objectives and competences
The objective of this course is to get familiar with the machine learning approaches and methods, to give students knowledge of the machine learning process and its fundamental tasks, and to qualify them for the use of machine learning methods and techniques as well as objective evaluation of results.
Content (Syllabus outline)
• Introduction to machine learning, basic concepts.
• Machine learning process: automated discovery of patterns in
data.
• Preparation and visualization of data, preparation and selection
of features, preparation of the training set.
• Supervised and unsupervised learning, structured and
unstructured data, data and models.
• Fundamental tasks and methods of machine learning:
classification, regression, clustering, decision trees, neural
networks.
• Evaluation and application of trained predictive models,
interpretation of results.
• Computer tools for using machine learning.
• Use cases of machine learning in education, sports, medicine,
finance.
• Transparency, explainability and bias of machine learning
models.
Learning and teaching methods
• lectures,
• case studies,
• lab work,
• individual work.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• identify problems, potentially appropriate for using machine
learning
• understand the applicability of machine learning methods
• use a machine learning method and prepare data to solve a given
task
• participate in the development project based on machine
learning
Transferable/Key skills and other attributes:
• Communication skills: oral presentation.
• Use of information technology: use of computer tools for using
machine learning methods.
• Problem solving: problem solving with the use of machine
learning, intelligent data analysis.
Readings
S. Karakatič, I. Fister: Strojno učenje: s Pythonom do prvega klasifikatorja, 1. izdaja, Univerzitetna založba Univerze v Mariboru, 2022
