Selected Topics in Machine Learning & Modelling in Biology (Exercise Session) [MA5607]
Course 0000004646 in WS 2019/20
General Data
| Course Type | exercise |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Research Department Mathematics Centre |
| Lecturers |
Carsten Marr Tingying Peng Fabian Theis |
| Dates |
Wed, 16:00–18:00, MI 02.06.011 |
Assignment to Modules
-
MA5607: Topics in Computational Biology (Selected Topics in Machine Learning and Modelling in Biology) / Topics in Computational Biology (Selected Topics in Machine Learning and Modelling in Biology)
This module is included in the following catalogs:- Elective Modules Natural Sciences in the Master Program Matter to Life
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | In all fields of life sciences, ranging from the analysis of genomic data over stem cell research to the treatment of disease, computational methods are employed to deepen our understanding of the respective biological processes and make predictions about the system’s dynamics. As the range of biological questions approached with computational biology is extremely broad, the number of different methods applied is likewise tremendous. In this lecture, we will give an overview of commonly used tools in computational biology, including gene sequence analysis, image computing, statistical network approaches and dynamic pathway modelling. In particular, we will introduce recent applications of deep learning to address biological questions. In parallel to the lecture, we offer an exercise course that gives the students hands-on experience in computational analyses and sharpens their analytic and programming skills. Topics includes: -Statistical inference for dynamical biological systems -Models of Stem Cell Decision Making -Quantitative models of transcriptional gene regulation -Hidden Markov Models for the analysis of epigenomics data -Polygenic Risk Analysis -Imputing single-cell gene expression -Deep learning based bioimage processing |
|---|---|
| Links | TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| WS 2018/9 | Topics in Computational Biology (Exercise Session) [MA5607] | Marr, C. Peng, T. Theis, F. |
Wed, 16:00–18:00, MI 02.06.011 |