Teaching / FIAS

Pattern Analysis and Machine Intelligence

with Prof. Kaschube, Prof. Ramesh and Prof. Roig (for additional information click here)

This is an online course. For further information please refer to the LSF or course homepage.

Programmierparadigmen und Compilerbau

(click here for additional information)

The lecture is held in German, please see the corresponding page.

Moodle page

Programmiersprachen 2

(click here for additional information)

This course is in German, please see the corresponding page.

Machine Learning I

with Prof. M. Kaschube (click here for additional information)

News:

Important: The second exam will take place on June 26 at 9:30 in HZ3

You need to register via QIS/LSF!
You need to sign this PDF and bring it along on the day of the exam!
The exam takes place in the lecture hall HZ3 at Campus Westend!

Sample exam questions can be found here PDF

We seek well-motivated students for Master- or Bachelor thesis projects to develop and apply deep learning methods for processing and analysing biological and medical imaging/microscopy data.

If you are interested, please contact Matthias Kaschube: kaschube_at_fias.uni-frankfurt.de

Lecture: Tue 16:15-17:45, Magnus Hörsaal

Exercises: Tue 14:15-15:45, H8

QiS/LSF Entry

Date	Lecture	Problem set	Tutorial
2019-10-15	Slides PDF	NA	Probability theory
2019-10-22	Slides PDF	NA	Linear algebra
2019-10-29	Slides PDF	Exercise sheet PDF
2019-11-05	Slides PDF	Exercise sheet PDF	Multivariate Gaussian
2019-11-12	Interactive Notebooks Link	Exercise sheet PDF
2019-11-19	Slides PDF
2019-11-26	Slides PDF	Exercise sheet PDF	Climate change Link, Link
2019-12-03	Slides PDF
2019-12-10	Slides PDF	Exercise sheet PDF
2019-12-17	Slides PDF
2020-01-14	Interactive Notebooks Link	Exercise sheet PDF
2020-01-21	Slides PDF	Exercise sheet PDF
2020-01-28	Slides PDF
2020-02-04	Slides PDF		Test exam PDF
2020-02-11	Research applications of ML PDF PDF

Pattern Analysis and Machine Intelligence

with Prof. Kaschube and Prof. Ramesh (for additional information click here)

Seminar: Thu 8:15-9:45, SR 11

QiS/LSF Entry

Date	Presenter	Topic	Supervisor
14.11.2020	Ritzke	A Conceptual introduction to HMC	Bertschinger
	Vogt	Generative Adversarial Networks	Bertschinger
21.11.2019	Schuhmann	A beginner's guide to the mathematics of neural networks	Ramesh
	van de Goor	Deep factors for forecasting	Ramesh
28.11.2019	Khadush	Generating visual explanations	Ramesh
	Gampe	Explainable AI: The new 42?	Ramesh
5.12.2019	Ludwig	Adaptive compression-based lifelong learning	Ramesh
	Leis	Deep neural network compression for aircraft collision avoidance systems	Ramesh
12.12.2019	Becker	Engineering a less artificial intelligence	Kaschube
	Pietschmann	Neuroscience-inspired artificial intelligence	Kaschube
19.12.2019	Iatrou	Weight agnostic neural networks	Kaschube
	Asimovska	Human-level control through deep reinforcement learning	Kaschube
16.01.209	Usov	Solving Bongard Problems with a Visual Language and Pragmatic Reasoning	Kaschube
23.01.2020	Vu Huu	FaceForensics++: Learning to Detect Manipulated Facial Images	Bertschinger
	Hoang	Everybody dance now	Bertschinger
30.01.2020	Tsang	Knet: beginning deep learning with 100 lines of Julia	Bertschinger
	Mansurova	∂P: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing	Bertschinger

News:

We seek well-motivated students for Master- or Bachelor thesis projects to develop and apply deep learning methods for processing and analysing biological and medical imaging/microscopy data.

If you are interested, please contact Matthias Kaschube: kaschube_at_fias.uni-frankfurt.de

Complex Networks - Methods and Algorithms

(click here for additional information)

Many complex systems in nature, technology or society can be represented as networks consisting of nodes connected by links. Such an approach has not only revealed structural regularities in different types of systems, e.g. food webs and social networks, suggesting common underlying mechanisms and concepts, but is also used to study the influence of the corresponding network structure on the behavior and function of the system. Recently methods from complex network theory have been applied to financial data and models, often to access systemic risk arising from the interconnections of the corresponding systems. This course represents an introduction to concepts and methods from complex network theory. Topics include: basic network models; sampling techniques; spreading, percolation and cascade processes on networks; network control; network models for financial systems.

QiS/LSF Entry

A substantial part of the lecture and exercises is based on the book "Networks: An Introduction" by M. E. J. Newman

Programming exercises will be done using R. We highly recommend to install RStudio, freely available from https://www.rstudio.com/, to get most out of the exercises.

Date	Lecture	Problem set	Tutorial
2019-10-17	Introduction PDF and PDF
2019-10-24	Slides PDF		R tutorial (R markdown, HTML)
2019-10-31	Slides PDF	Ex. sheet PDF
2019-11-07	Slides PDF		Solution Ex. 1 PDF
2019-11-14	Slides PDF	Ex. sheet PDF
2019-11-21	Slides PDF		Solution Ex. 2 PDF
2019-11-28	Slides PDF	Ex. sheet PDF
2019-12-05	Slides PDF		Solution Ex. 3 PDF
2019-12-12	Slides PDF	Ex. sheet PDF
2020-01-16	Slides PDF		Solution Ex. 4 PDF
2020-01-23	Slides PDF	Ex. sheet PDF
2020-01-30	Slides PDF		Solution Ex. 5 PDF
2020-02-06	Slides PDF	Ex. sheet PDF
2020-02-13	Slides PDF		Solution Ex. 6 PDF

Programmiersprachen 1

(click here for additional information)

Please switch to the German page.

Pattern Analysis and Machine Intelligence

with Prof. V. Ramesh (click here for additional information)

Seminar: Thu 8:15-9:45, SR 11

QiS/LSF Entry

List of topics

Bertschinger: Topics.txt
Ramesh: Link

Course schedule

	Date	Presenter	Topic	Supervisor
1	02/05/19	Rechberger	AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling	Ramesh
1	02/05/19	Baral	Dropout: A Simple Way to Prevent Neural Networks from Overfitting	Ramesh
2	09/05/19	Krause	Knet: beginning deep learning with 100 lines of Julia	Bertschinger
2	09/05/19	Jaziri	Deep Probabilistic Programming	Bertschinger
2	09/05/19	Ioannou	Probabilistic Programming – Stan	Bertschinger
3	16/05/19	Zhang	A Conceptual Introduction to Hamiltonian Monte Carlo	Bertschinger
3	16/05/19	Hammerla	A Conceptual Introduction to Hamiltonian Monte Carlo	Bertschinger
3	16/05/19	Ulmer	Adam: A Method for Stochastic Optimization	Bertschinger
4	23/05/19	Römer	Reality-check for Econophysics: Likelihood-basedfitting of physics-inspired market models to empirical data	Bertschinger
4	23/05/19	Baumgartner	How Bayesian Analysis Cracked the Red-State, Blue-State Problem	Bertschinger
5	06/06/19	Huth	Towards a Systematic Evaluation of Generative Network Models	Bertschinger
5	06/06/19	Schröber	Stochastic blockmodels and community structure in networks	Bertschinger
6	13/06/19	Dahmann	This looks like that: deep learning for interpretable image recognition	Ramesh
6	13/06/19	Erol	This looks like that: deep learning for interpretable image recognition	Ramesh
7	27/06/19	Tel	Robust Physical-World Attacks on Deep Learning Visual Classification	Bertschinger
7	27/06/19	Müller	Avoiding pathologies of deep architectures	Bertschinger
8	04/07/19	Pokhrel	Building Machines That Learn and Think Like People	Ramesh
8	04/07/19	Heissel	Building Machines That Learn and Think Like People	Ramesh
9	11/07/19	Zarbock	Unmasking Clever Hans predictors and assessing what machines really learn	Ramesh
9	11/07/19	Mozzhorin	"Why Should I Trust You?" Explaining the Predictions of Any Classifier	Ramesh
10	18/07/19	Samir	Snorkel: Rapid Training Data Creation with Weak Supervision	Ramesh
10	18/07/19	Meyer	The Mythos of Model Interpretability	Ramesh

Machine Learning II

with Prof. V. Ramesh (click here for additional information)

Lecture: Wed 16:15-17:45, NM 114

Tutorial: Wed 14:15 - 15:45, NM 114

QiS/LSF Entry

Date	Lecture	Problem set	Tutorial
17/04/2019	Introduction PDF	Ex. sheet 1 PDF	Probability theory
24/04/2019	Slides PDF	Ex. sheet 2 PDF
08/05/2019	Slides PDF	Ex. sheet 3 PDF	data_simpson.csv
15/05/2019	Slides PDF	Ex. sheet 4 PDF
22/05/2019	Notebooks (PyMLVizard)	Exercises (Link) (Link)
29/05/2019	Notebooks (PyMLVizard)
12/06/2019	Slides PDF	Ex. sheet 5 PDF
19/06/2019	Random forests (Notebooks)	Exercises (Notebooks)	Study material (external lecture)
26/06/2019	Material Ramesh (Link)

Note: The retake exam will be held on Wed, Sept. 11, at 14:00 in room NM 114 (same as the lecture).

Bayesian Methods in Economics and Finance

(click here for additional information)

Mo 12:15-13:14, SH 1.105

QiS/LSF Entry

Date	Material
15/04/2019	Introduction (PDF)
29/04/2019	Priors (PDF)
06/05/2019	Stan modeling (PDF) (R script)
13/05/2019	Model selection (PDF)
20/05/2019	Sampling (PyMLVizard)
27/05/2019	Sampling (PyMLVizard)
03/06/2019	NO CLASS!

01/07/2019	Hierarchical modeling (HTML) (R markdown)
08/07/2019	Vector Autoregression (PDF) (R script)
15/07/2019	Bayesian nonparametrics (PDF)

Programmiersprachen 2

(click here for additional information)

This course will be held in German, please switch to that page.

Machine Learning I

with Prof. M. Kaschube and Prof. V. Ramesh (click for additional information)

Lecture: 16:15-17:45, Magnus-Hörsaal

Tutorial: 14:15 - 15:45, H13 and SR307

QiS/LSF Entry

Date	Lecture	Problem set	Tutorial
2018-10-16	Introduction	NA	Probability theory
2018-10-23	Slides	Ex. sheet 1	Linear Algebra
2018-10-30	Slides	Ex. sheet 2
2018-11-06	Notebook (PDF)	Ex. sheet 3
2018-11-13	Slides	Ex. sheet 4
2018-11-20	Slides	Ex. sheet 5
2018-11-27	Slides	Ex. sheet 6
2018-12-04	Slides
2018-12-11	Slides	Ex. sheet 7
2018-12-18	Slides	Ex. sheet 8
2019-01-15	Notebook (Link) (PDF)	Ex. sheet 9
2019-01-22	Notes (HTML) (R markdown)	Ex. sheet 10
2019-01-29	Slides	Ex. sheet 11
2019-02-05	Slides
2019-02-12	Slides

The interactive notebooks are available from PyMLVizard.
Note that to run the notebooks in binder you will need to switch to the develop branch.

NEWS: Information about the final exam can be found here QiS/LSF Entry

Complex Networks - Methods and Algorithms

(click here for additional information)

Many complex systems in nature, technology or society can be represented as networks consisting of nodes connected by links. Such an approach has not only revealed structural regularities in different types of systems, e.g. food webs and social networks, suggesting common underlying mechanisms and concepts, but is also used to study the influence of the corresponding network structure on the behavior and function of the system. Recently methods from complex network theory have been applied to financial data and models, often to access systemic risk arising from the interconnections of the corresponding systems. This course represents an introduction to concepts and methods from complex network theory. Topics include: basic network models; sampling techniques; spreading, percolation and cascade processes on networks; network control; network models for financial systems.

QiS/LSF Entry

A substantial part of the lecture and exercises is based on the book "Networks: An Introduction" by M. E. J. Newman

Programming exercises will be done using R. We highly recommend to install RStudio, freely available from https://www.rstudio.com/, to get most out of the exercises.

Date	Lecture	Problem set	Tutorial
2018-10-18	Introduction PDF and PDF
2018-10-25	Slides PDF		Introduction to R Rmd and HTML
2018-11-01	Slides PDF	Ex. sheet 1 PDF
2018-11-08	Slides PDF		Solution ex. sheet 1 PDF
2018-11-15	Slides PDF	Ex. sheet 2 PDF
2018-11-22	Slides PDF		Solution ex. sheet 2 PDF R source
2018-11-29	Slides PDF
2018-12-06	Slides PDF		Linear Algebra PDF
2018-12-13	Slides PDF	Ex. sheet 3 PDF
2018-12-20	Slides PDF	Ex. sheet 4 PDF	Solution ex. sheet 3 PDF
2019-01-17	Slides PDF		Solution ex. sheet 4 PDF
2019-01-24	Slides PDF	Ex. sheet 5 PDF
2019-01-31	Slides PDF		Solution ex. sheet 5 PDF
2019-02-07	Slides PDF

Pattern Analysis and Machine Intelligence

with Prof. Kaschube and Prof. Ramesh (click for additional information)

QiS/LSF entry

Course schedule

Date	Name	Topic	Supervisor
15.11.2018	D. Machajewski	The Surprising Creativity of Digital Evolution	Ramesh
15.11.2018	M. Machajewski	The Surprising Creativity of Digital Evolution	Ramesh
29.11.2018	Hahner	Hamiltonian Monte-Carlo	Bertschinger
29.11.2018	Allgöwer	Fitting econophysics models	Bertschinger
06.12.2018	Pitschmann	Stochastic blockmodels	Bertschinger
06.12.2018	Stobbe	Bayesian models of graphs	Bertschinger
13.12.2018	Altincan	Accessorize to a crime	Kaschube
13.12.2018	Vogel	DeepLabCut	Kaschube
17.01.2019	Klose	Deep Q-Learning	Ramesh
17.01.2019	Ditzel	Genetic CNN	Ramesh
24.01.2019	Banzhaf	Red-State, Blue-State Problem	Kaschube
24.01.2019	Konca	Dropout	Kaschube
31.01.2019	Fermpas	Towards Accountable AI	Ramesh
31.01.2019	Hess	Simultaneous Deep Transfer Across Domains and Tasks	Ramesh

Programmiersprachen 1

This course is in German, please refer to the German page.

Machine Learning II

with Prof. M. Kaschube (click for additional information)

Lecture: 16:15-17:45, Neue Mensa - NM 113

Tutorial: 14:15 - 15:45, Neue Mensa - NM 114

QiS/LSF Entry

Date	Lecture	Problem set	Tutorial
2018-04-11	Introduction	-	Probability Theory
2018-04-18	Bayesian inference and conjugate priors	Exercise sheet 1	Linear Algebra
2018-04-25	Linear Regression	Exercise sheet 2	-
2018-05-02	Bayesian linear regression (demo) Data Modeling	Exercise sheet 3	Data set
2018-05-09	Mixture Models	Exercise sheet 4
2018-05-16	Expectation Maximization Algorithm	Exercise sheet 5	Data set Implementation
2018-05-23	Sampling part 1 Sampling part 2	Exercise sheet 6	config_check.py
2018-05-30	Sampling part 3 Sampling part 4	Exercise sheet 7
2018-06-06	Sampling part 5 Sampling part 6	Exercise sheet 8
2018-06-13	Gaussian processes	Exercise sheet 9
2018-06-27	Soccer modeling		Data sets and code examples
2018-07-04	Gaussian processes	Exercise sheet 10
2018-07-11	SBM

Seminar Pattern Analysis and Machine Intelligence

with Prof. M. Kaschube and Prof. V. Ramesh (click for additional information)

Seminar: Thu, 8:15 - 9:45, SR11

QiS/LSF Entry

Date	Name	Topic	Supervisor
26.04.2018	No course
03.05.2018	Robert am Wege	Deep Learning: An Introduction for Applied Mathematicians	Ramesh
03.05.2018	Ionut Petre Urs	Deep Learning: An Introduction for Applied Mathematicians	Ramesh
17.05.2018	Timothy Mason	Dropout: A Simple Way to Prevent Neural Networks from Overfitting	Bertschinger
17.05.2018	Elisabeth Schmidt	Dropout as a Bayesian Approximation: Insights and Applications	Bertschinger
24.05.2018	Gianluca Romano	Texture synthesis using CNNs	Kaschube
24.05.2018	Christopher Grosse	Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition	Kaschube
07.06.2018	oor-ul-Sabha Amjad	Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models	Bertschinger
14.06.2018	No course
21.06.2018	Joshua Mendaza	Recent Advances in Recurrent Neural Networks	Ramesh
28.06.2018	Yulia Kim	Representation learning: A review and new perspectives	Kaschube
05.07.2018	Ramin Kanani	A Variational Analysis of Stochastic Gradient Algorithms	Bertschinger
05.07.2018	Hannah Wendland	A Conceptual Introduction to Hamiltonian Monte Carlo	Bertschinger
12.07.2018	No course

Machine Learning I

with Prof. M. Kaschube and Prof. V. Ramesh (click for additional information)

Winter Semester 2017/2018

Instructors: Matthias Kaschube, Nils Bertschinger

Lecture: 16:15-17:45, Magnus Hörsaal

Tutorial: 14:15 - 15:45, SR 11

Exam: 20th February, 14:00-16:00, Magnus Hörsaal (90 min)

13th March, 14:00-16:00, Magnus Hörsaal (90 min)

Date	Lecture	Problem Set	Tutorial
2017-10-17	week1.pdf	-	-
2017-10-24	week2.pdf	Exercise 1	Linear Algebra
2017-10-31	Reformation Day Holiday
2017-11-07	week3.pdf	Exercise 2	Exercises
2017-11-14	week4.pdf	-	-
2017-11-21	week5.pdf	Exercise 3	How-To: Posterior Distribution
2017-11-28	week6.pdf	Exercise 4	How-To: Linear Model
2017-12-05	week7.pdf	Exercise 5
2017-12-11	week8.pdf	Exercise 6
2017-12-19	week9.pdf	Exercise 7	Training Data
2018-01-09	week10.pdf	-	-
2018-01-16	week11.pdf	Exercise 8
2018-01-23	week12.pdf		Minimal Solution for Exercise 7
2018-01-30	week13.pdf	How-To: Classification	Tensor Flow Introduction

Seminar "Pattern Analysis and Machine Intelligence"

with Prof. M. Kaschube and Prof. V. Ramesh

Programmiersprachen 2

Complex Networks -- Methods and Algorithms

(click for additional information)

Many complex systems in nature, technology or society can be represented as networks consisting of nodes connected by links. Such an approach has not only revealed structural regularities in different types of systems, e.g. food webs and social networks, suggesting common underlying mechanisms and concepts, but is also used to study the influence of the corresponding network structure on the behavior and function of the system. Recently methods from complex network theory have been applied to financial data and models, often to access systemic risk arising from the interconnections of the corresponding systems. This course represents an introduction to concepts and methods from complex network theory. Topics include: basic network models; sampling techniques; spreading, percolation and cascade processes on networks; network control; network models for financial systems.

QiS/LSF Entry

A substantial part of the lecture and exercises is based on the book "Networks: An Introduction" by M. E. J. Newman

Programming exercises will be done using R. We highly recommend to install RStudio, freely available from https://www.rstudio.com/, to get most out of the exercises.

Further material, e.g. lecture slides and exercise sheets, will be made available here ...

Lecture, Oct 19: Slides PDF, PDF
Exercise, Oct 26: R Markdown, HTML R Markdown files combine markup formatted text with R code. Rstudio can readily edit such documents and convert them to HTML with embedded code and figures. Exercise sheet 1 PDF
Lecture, Oct 26: Slides PDF
Lecture, Nov 2: Slides PDF
Exercise, Nov 9: Solution for sheet 1 PDF Exercise sheet 2 PDF
Lecture, Nov 9: Slides PDF
Nov 16: No class Note: There will be two lectures on Nov 23
Lecture, Nov 23: Slides PDF, Slides PDF, DebtRank simulation R script
Nov 30: No class. Due to illness the lecture has to be cancelled.
Exercise, Dec 7: Solution for sheet 2 PDF Exercise sheet 3 PDF
Lecture, Dec 7: Slides PDF
Lecture, Dec 14: Slides PDF
Exercise, Dec 21: Solution for sheet 3 PDF Exercise sheet 4 PDF
Lecture, Dec 21: Slides PDF
Lecture, Jan 11: Slides PDF
Exercise, Jan 18: Solution for sheet 4 PDF Exercise sheet 5 PDF
Lecture, Jan 18: Slides PDF
Lecture, Jan 25: Slides PDF Test exam PDF
Exercise, Feb 1: Solution for sheet 5 PDF
Lecture, Feb 1: Slides PDF

Machine Learning II

with Prof. M. Kaschube and Prof. V. Ramesh (click for additional information)

QiS/LSF Entry

Further material, e.g. lecture slides, data sets, will be made available here.

Material:

April 19: Exercise slides PDF Lecture slides PDF
April 26: Exercise slides PDF Exercise sheet PDF Lecture slides PDF
May 3: Exercise sheet PDF Lecture slides PDF
May 10: Exercise sheet PDF Note: There is an error in Ex. 3.3. You will have time to solve the corrected version till May 24. Lecture slides PDF Interactive Visualization R code (To run it start R and then load it with source("shinyLR.R", echo=TRUE) ... hope it works for you)
May 17: Exercise sheet PDF Lecture slides PDF Demo script Python code
May 24: Exercise sheet PDF Regularization demo IPython Notebook Lecture slides PDF
May 31: Exercise sheet PDF Lecture slides PDF
June 7: Exercise sheet PDF Lecture notes IPython Notebook (PDF)
June 14: Exercise sheet PDF Lecture notes IPython Notebook (PDF)
June 21: Exercise sheet PDF Training data CSV, CSV Lecture notes IPython Notebook (PDF)

Seminar "Pattern Analysis and Machine Intelligence"

with Prof. M. Kaschube and Prof. V. Ramesh

Bayesian methods in Economics and Finance

(click for additional information)

Bayesian methods are becoming more and more popular, mainly thanks to modern algorithms and increasing computer power, as tools for statistical modeling and inference. This course will introduce the Bayesian philosophy of statistical modeling and important algorithms, e.g. Monte-Carlo sampling and variational methods, which will be illustrated on several examples from finance and econometrics.

QiS/LSF Entry

Further material, e.g. lecture slides, data sets, will be made available here.

Material:

April 27: Lecture slides PDF

May 4: Lecture slides PDF

May 11: Lecture slides PDF + accompanying R code

May 18: Lecture slides PDF + accompanying Python code

Interactive Visualization R code ... to run it start R and then load it with source("shinyLR.R", echo=TRUE)

June 1: Lecture notes PDF + Jupyter Notebook

June 29: Lecture notes HTML + R markdown

June 13: Lecture notes HTML + R markdown

July 20:Lecture slides PDF + accompanying R code

Lecture slides PDF

Lecture notes HTML + R markdown

Note: There will be an additional lecture from 14 to 16 o'clock in room 4.106

Programmiersprachen 1

Machine Learning I

with Prof. M. Kaschube and Prof. V. Ramesh

Seminar "Pattern Analysis and Machine Intelligence"

with Prof. M. Kaschube and Prof. V. Ramesh

Complex Networks -- Methods and Algorithms

Informationstheorie

Oliver Pfante (click for additional information)

In dieser Vorlesung werden die grundlegenden Begriffe der Informationstheorie eingeführt und ihre Anwendung in der Datenkompression, Signalverarbeitung und Finanzmathematik dargelegt.

Zeitplan (Die Vorlesung am Donnerstag ist in den Seminarraum NM 111 verlegt worden!) Vorlesung: 1. Kapitel, 2. Kapitel, 3. Kapitel, 4. Kapitel, 5. Kapitel Übung: 1. Übung, 2. Übung, 3. Übung, 4. Übung, 5. Übung, 6. Übung, 7. Übung Lösung: 1. Lösung, 2. Lösung, 3. Lösung, 4. Lösung, 5. Lösung, 6. Lösung, 7. Lösung

Nachklausur: 28.03.2017 von 10oo bis 12oo im H7 (Uhrzeiten sind scharf, d. h. ohne akademische Viertelstunde!). Skript und Übungsblaetter dürfen nicht mitgeführt werden. Teilnahmebedingung sind mindestens 56 erlangte Übungspunkte.

Helmuth O. Maucher Professorship - Nils Bertschinger - Teaching

SoSe 20

WiSe 19/20

Important: The second exam will take place on June 26 at 9:30 in HZ3

SoSe 19

WiSe 18/19

SoSe 18

WiSe 17/18

SoSe 17

WiSe 16/17

SoSe 16

WiSe 15/16