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.
• Bias and fairness of machine learning models.
Learning and teaching methods
• lectures,
• case studies,
• lab work,
• individual work.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• understand the use of green machine learning
• data processing into an appropriate format with appropriate
tools
• using tools and cloud services for green machine learning needs
Transferable/Key skills and other attributes:
• Data processing: data processing for the needs of green
application and statistical review of data,
• Selection and use of machine learning algorithms: use of suitable
machine learning techniques with the help of interactive tools,
• Interpretation and visualization of results: the ability to interpret
and visualize results and the ability to judge the appropriateness of
results.
Readings
• O. Theobald: Machine Learning for Absolute Beginners: A Plain English Introduction, 2nd Edition, Scatterplot Press, 2017.
• C. Albon: Machine Learning with Python Cookbook: Practical Solutions from Pre-processing to Deep Learning, O’Reilly Media, 2018.
• S. Karakatič, I. Fister: Strojno učenje: s Pythonom do prvega klasifikatorja, 1. izdaja, Univerzitetna založba Univerze v Mariboru, 2022.
