Master
2022/2023
Machine Learning in Bioinformatics
Type:
Elective course (Data Analysis in Biology and Medicine)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
1 year, 1, 2 module
Mode of studies:
distance learning
Online hours:
14
Open to:
students of one campus
Master’s programme:
Data Analysis for Biology and Medicine
Language:
English
ECTS credits:
6
Contact hours:
56
Course Syllabus
Abstract
The course introduces the theory and practice of machine learning algorithms and their applications in the area of bioinformatics. The students will learn data preprocessing techniques, methods of dimension reduction, technique of modeling using machine-learning algorithms, parameter tuning. The studied algorithms include linear regression with regularization (ridge regression, elastic net, lasso), multivariate adaptive regression splines, support vector machines, neural networks, k-nearest neighbors, classification and regression trees, random forest, gradient boosting. Workshops, which follow the lectures, seek to empower students with the practical skills in predictive modeling software tools, packages and applications. Many case studies of predictive models for bioinformatics data sets will be considered.