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:
On completion of this course the student will be able to
• 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 seminar work defence.
• 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
• 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.
