Teaching Summer Semester 2019

Courses offered by Bioinspired Communication Systems Lab Summer Semester 2020

 

Computational Methods for Systems and Synthetic Biology – vl

Course No: 18-kp-2080-vl

Time: Wednesdays 1:30 pm-3:10 pm

The course covers mathematical methods used in the area of systems and synthetic biology. On the one hand it deals with practical modeling of molecular processes but also with theoretical investigations that reveal general properties of those processes. The course follows a microscopic approach and introduces those processes using probabilistic methods. For that, necessary pre-requists are recapitulated, such as definition of Markov processes in different spaces and their properties. With this background, the dynamics of stochastic reaction kinetics in terms of popula-tion models is investigated. Limiting cases are introduced, such as the diffusion approximation or the deterministic approximation (fluid approximations) of those systems. Often methods from statistical physics are applied. Numerical methods for solving the corresponding Fokker-Planck and Master equations are discussed. For the limiting case of a deterministic approximation, tradi-tional methods for the stability analysis of nonlinear differential equations are introduced and methods are discussed that just rely on the topology of the reaction network to determine stabil-ity properties. In this context, a derivation of the moment dynamics and approximation methods based on moment closure are given. Connections to queueing theory models are shown. Furthermore, the question is addressed of how the introduced dynamical models are calibrated to data from molecular biology. For that, general methods of statistical inference from statistics and of machine learning from computer science are discussed and specialized algorithms for the con-sidered system class are presented. Additionally, a short introduction to the theory of nonlinear optimal filtering is given and special cases such as hidden Markov models are discussed. Beyond reaction kinetics, the course provides a basic introduction to the modeling and numerical methods used in molecular dynamics. Newtonian multi-body simulations and classical potentials and their use in molecular dynamics are discussed. Most of the topics in this course are intro-duced through practical examples from applied modeling in the domain of systems biology. The applicability of the respective methods in synthetic biology is highlighted.

Computational Methods for Systems and Synthetic Biology – ue – exercise

Course No: 18-kp-2080-ue

Time: Fridays 9:50 am – 11:30 am

Practical modeling of molecular processes and to determine dynamical properties of model using mathematical methods.

It relies on the understanding of the following topics:

• Mathematical abstraction of molecular mechanisms

• General properties of stochastic processes

• Approximation methods for Markovian population models

• Stability analysis of nonlinear differential equations

• Numerical methods for solving/simulating stochastic systems

• System identification/machine learning for stochastic systems

Computational Modeling for the IGEM Competition

Course No: 18-kp-2100-se

Time: please fix a date

 

Machine Learning in Information and Communication Technology (ICT) – lecture

Course No: 18-kp-2110-vl

Time: Mondays 16:15 pm – 17:55 pm

Instructors: Prof. A. Klein, Prof. A. Zoubir, Prof. M. Pesavento, Prof. H. Köppl

Contents: Introduction into machine learning from the perspective of an engineer.
Most important models and learning procedures will be presented and explained by problems in information and communication systems.

Literature:
Kevin P. Murphy: Machine Learning – A probabilistic perspective, MIT Press, 2012
Christopher M. Bishop: Pattern recognition and Machine Learning, Springer, 2006
Peter Bühlmann und Sara van de Geer: Statistics of high-dimensional data – Methods, theory and applications, Springer, 2011

Machine Learning in Information and Communication Technologies – exercise

Course No: 18-kp-2110-ue

The module provides an introduction to the emerging field of machine learning from an engineering perspective. Important models and learning methods are presented and exemplified through problems from information and communication technology.

- Fundamentals of probability theory and multivariate statistics

-Taxonomy of machine learning problems and models (supervised, unsupervised, generative, discriminative)

- Probabilistic graphical models: categories, inference and parameter estimation, structure learning, probabilistic programming

- Hidden Markov models (HMM): Theory, Algorithms and ICT applications (e.g. Viterbi decoding of convolutional codes)

- Fundamentals of Bayesian inference, Monte Carlo methods, Bayesian non-parametrics

- Regression and classification: theory, methods and ICT applications

- Dimensionality reduction, clustering and big data analytics: methods and application in communications and signal processing

- Approximate algorithms for scalable Bayesian inference; application in signal processing and information theory (e.g. decoding of LDPC codes)

- Inference as optimization, variational inference

- Deep neural networks (deep learning): Models, learning algorithms, libraries and ICT applications

Machine Learning in ICT – practical training

Course No: 18-kp-2110-pr

Time: please fix a date

 

Proseminar ETiT

Course No: 18-kp-1000-ps Proseminar ETiT

You can register for a proseminar at BCS at any time.

If you are interested in doing a proseminar under supervision of BCS, please, do not hesitate to contact any member of BCS and ask for details.

Content:

Read published books or papers on a given subject in Electrical Engineering and Information Technology. Write a summary and present it using multi media technology.

Project seminar Communication and Sensor Systems

Course No: 18-kp-1041-pj

Investigating and solving specific problems concerning communication and sensor systems (Problems concerning communications engineering, microwave technology, signal processing, sensor networks etc. are possible, topics will be defined out of the recent research topics of the involved labs), working on a a given task by one’s own, organizing and structuring of a seminar task, searching and analyzing of scientific reference publications for a given task, summarizing achieved results and conclusions by means of a written report, presenting achieved results and conclusions and defending them in an oral discussion including audience.

Telecommunications – seminar

Course No: 18-kl-9010-se

Monday 2-5 pm

In this seminar students working on Bachelor thesis, Master thesis, Studienarbeit and Diplomarbeit, research assistants of the institute and guest researchers present their work. The presentation topics are indicated by announcement.

All students interested in, or currently working on a thesis in the field of communication technology are encouraged to participate in this seminar.

Introduction to Scientific Computing with Phyton

Course No: 18-st-2070-pr

Monday 1:30-5 pm

Scientific computing is introduced via six case studies. Exemplary engineering problems that are know from basic engineering courses are solved on a computer using fundamental methods from numerical mathematics. Opportunities and limitations of this approach are highlighted.

The required material on numerical mathematics is taught via preparatory scripts for each case study. During the practical exercises the methods are implemented in the current computing environment Python under the guidance of suitable teaching personnel.

The case studies cover the following numerical topics:

- Formulation and solution of systems of linear equations, sparse methods

- Integration of ordinary differential equations (ODE) and their analysis based on eigenvalues

- Mathematical optimization and automated differentiation

- Linear regression and approximation, first Machine Learning algorithms

- Discretization of simple partial differential equations (PDE)

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