Computational Biology of Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.


Seminar: Deep Learning

The seminar "Deep Learning" is hosted by the department "Computational Biology of Infection Research" at the HZI headed by Prof. Alice McHardy.

Kick-off Meeting: 12th October 2018, 10 a.m.
Room Kick-off Meeting: BRICS, room 207
Date: one day at the end of semester break
Room seminar: tba
Max. number of participants: 10
Language: English
Modus: 30 minutes of presentation (with discussion) + 3-5 pages summary
Designated for Bachelor and Master Students of Computer Science

In case you have questions about the seminar, feel free to contact Susanne Reimering.

Description: Recently, deep neural network models have revolutionized machine learning research and achieved the state-of-the-art performance in almost every related research, including computer vision, natural language processing, and computational biology. The goal of this seminar is to  teach the basic principles of deep learning along with some basic implementations in pytorch framework. We will explore the most important neural architectures including convolutional neural networks, recurrent neural networks, and autoencoders as well as language-model based representation learning methods.


•    Deep Neural Networks and back propagation
•    Convolutional Networks (CNN)
•    Recurrent Neural Networks (RNN)
•    Autoencoders
•    Representation Learning
•    Introduction to pytorch

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