Machine Learning and Big Data (SL_120)
About Study Course
Objective
Machine learning (ML) involves the study of algorithms that can extract information automatically and induce new knowledge from data. ML tasks are often related to large datasets, that create challenges in the areas of data storage, organization and processing. The response to these challenges is addressed by the discipline of the big data analytics. The aim of this course is to introduce students to the most important methods of machine learning: variations of regression and classification algorithms, as well as introduce the concepts of deep learning and big data analytics. The methods will be explored by case studies implemented in R program.
Prerequisites
Higher mathematics, probability, statistics, basic knowledge of R programming.
Learning outcomes
• Selects the resampling methods and criteria of model accuracy assessment.
• Explain the most important regression and classification algorithms.
• Identifies the Big Data concept.
• Can independently implement regression and classification machine learning algorithms in R.
• Analytical evaluation R computational limitations and selects strategies to overcome those.
• Can critically compare various machine learning strategies and choose the appropriate algorithm for the problem at hand.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Biostatistics, MFBS | 3 | Master’s | Required | Artis Alksnis |
