Deep learning in life science 1000-2M23DLS
We will learn about the current approaches using deep learning methods (convolutional neural networks, recurrent neural networks, transformer-based networks) for classical problems in molecular biology and medicine (microscopy image classification, protein structure prediction and analysis, biological sequence analysis and classification).
The video lectures are available online (https://deeplife4eu.github.io/program) Labs are going to be held in person.
Type of course
Course coordinators
Learning outcomes
Students will gain knowledge of contemporary deep learning methods, be able to implement deep learning models, and evaluate their quality for biological applications. Students will also gain experience and competence in presenting the principles of model operation and the results of computational experiments involving training networks on experimental data and optimizing the parameters of such models.
Assessment criteria
Individual homework scores (50 %) and a final team project (50%), 60% total points are required for a passing grade.
Bibliography
Materials available online: https://deeplife4eu.github.io/program.
Notes
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Term 2025L:
This year there will be no possibility of our students to participate in the hacakthon part. |
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: