Selected Topics in Machine Learning & Modelling in Biology [MA5607]

Lecturer (assistant)
  • Fabian Theis
  • Carsten Marr
  • Tingying Peng
Number0000002414
TypeLecture
Duration2 SWS
TermWintersemester 2019/20
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline

Dates

Admission information

Objectives

After the successful completion of the module, the participants - understand a selection of methods used in computational biology - understand advantages and disadvantages of the introduced methods - can evaluate which methods can be used to approach a given problem

Description

In all fields of life sciences, ranging from yeast strain optimization for brewing (→ bioprocess engineering) over stem cell research (→ basic biology) to the treatment of disease (→ medicine), computational methods are employed to deepen our understanding of the respective biological processes/system. As the range of biological questions approached using computational biology is rather broad, the number of different methods applied in this field is tremendous. Commonly used tools include gene sequence analysis, image analysis, statistical network modeling and dynamic pathway modeling. All of these tools span one or more fields of mathematics, e.g., statistics, differential equations and optimization. This lecture series aims at providing the participants with an overview about different fields of computational biology and the methods used in this field. To complement the theoretical part, concrete application and ongoing research projects will be presented. The individual lectures of the lecture series will be taught by persons from the: - M12 Biomathematics, Center of Mathematical Sciences, TUM - Institute of Computational Biology, Helmholtz Center Munich

Prerequisites

Bachelor in mathematics, bioinformatics, statistics or related fields.

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