Deep learning in life science
General data
Course ID: | 1000-2M23DLS |
Erasmus code / ISCED: |
11.3
|
Course title: | Deep learning in life science |
Name in Polish: | Deep learning in life science |
Organizational unit: | Faculty of Mathematics, Informatics, and Mechanics |
Course groups: |
(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science Elective courses for Machine Learning Specific programme courses of 2nd stage Bioinformatics |
ECTS credit allocation (and other scores): |
6.00
|
Language: | English |
Type of course: | elective monographs |
Classes in period "Summer semester 2023/24" (in progress)
Time span: | 2024-02-19 - 2024-06-16 |
Navigate to timetable
MO WYK
TU W TH LAB
LAB
FR |
Type of class: |
Lab, 30 hours
Lecture, 30 hours
|
|
Coordinators: | Bartosz Wilczyński | |
Group instructors: | Marcin Możejko, Maciej Sikora, Joanna Sułkowska, Bartosz Wilczyński | |
Course homepage: | https://deeplife4eu.github.io/ | |
Students list: | (inaccessible to you) | |
Examination: | Examination | |
Main fields of studies for MISMaP: | biology |
|
Prerequisites: | Introduction to computer science 1000-711WIN |
|
Course dedicated to a programme: | 4EU+Courses |
|
Short description: |
As part of the Seed4EU+ action within the 4EU+ alliance of European universities, Heidelberg University will coordinate a joint course on Applications of Deep Learning in Life Sciences (DeepLife) involving the universities of Paris-Sorbonne, Warsaw, Prague, Milano and Heidelberg. The purpose of this course is to give an insight in the very active field of DL in the field of biomedicine and highlight recent examples of applications in three distinct areas: single-cell genomics protein bioinformatics biomedical image analysis. |
|
Full description: |
Introduction: 1. Intro and Mathematical foundation to DL 2. Convolutional and Recurrent neural networks 3. Autoencoders and variational autoencoders 4. Attention mechanisms and transformers Genomics: 5. Transformers and RNN for sequence analysis 6. Models for multimodal data integration 7. VAE in single-cell genomics Protein Structure: 8. AlphaFold, EMSFold to predict structure of proteins 9. RNN, CNN models for topology/graph analysis in biopolymers 10. Deep learning models for protein-ligand binding site prediction 11. Diffusion models for protein design BioImage analysis: 12.Intro to BioImage Analysis and Deep Learning Utilization 13.Deep Architectures for sampling macromolecules 14. Deep learning for segmentation |
|
Bibliography: |
Deep Learning book by Goodfellow, Bengio, Courville The Elements of Statistical Learning by Hastie, Tibshirani, Friedman An Introduction to Statistical Learning by Hastie, Tibshirani, Friedman (a simpler version of the previous book) Machine learning with PyTorch and scikit-learn by Raschka, Liu, Mirjalili (a great introduction into the technical aspects of DL in pyTorch). |
|
Notes: |
The credits for the course are granted based on homeworks done throughout the semester. The best students will be able to go in June to the hackathon in Heidelberg. |
Copyright by University of Warsaw.