||"The worm thinks it foolish that Man does not eat his books."
Talks and Presentations
Talks 2014 - 2017Optimization With R — Tips and Tricks
Cologne R User Group, September 2017
There are almost 100 packages listed on the CRAN optimization task view. We will discuss some of the more important packages in areas such as nonlinear optimization with and w/o constraints, least-squares problems, non-smooth and global optimization, and mixed integer programming. Tips and tricks will be provided for high precision or high-dimensional problems, for equality constraints, for minimax problems, etc.
Slides of the talk (4 slides per page, 300 KB)
Infos on new packages presented at UseR 2016
Private Communication, August 2016
Some interesting packages presented at the UseR! 2016 at Stanford University will be described in one slide each, including profvis, covr, broom, future, feather. For data mining, packages like xgboost, ranger, or mxnet are useful new contributions. Links to Microsoft Channel 9 are included.
Private Communication, April 2016
Short slide show (Jupyter notebooks will be added later)
R Training - Basic Introduction
ABB Forschungszentrum, Ladenburg, January 2016
Eine kurze Einführung in R Syntax, Einlesen von Daten, Dataframes, statistische Graphen, Programmierung mit R, Beispiele der Regression, etc.
Nonlinear Optimization with R - An engineering example
Wiesbaden R Users Group, October 2015
A prediction task for gas distribution networks with storage tanks will be solved as a nonlinear optimization problem with constraints, by applying oprimization solvers available in R packages. Several different objective functions will be tested. Also shown is how this problem can be formulated in the AMPL modeling language and be sent to NEOS solvers of the COIN-OR project.
Jupyter notebook An Example in Constraint Optimization
Numerik mit MATLAB
DHBW Kurs, 20 Stunden, Mannheim, Herbst 2015
Folien Numerik (mit MATLAB), Aufgaben und Tests nicht verfügbar
Introduction to Julia for R Users
Cologne R User Group, December 2014
Julia is a high-performance dynamic programming language for scientific computing, with a syntax that is familiar to users of other technical computing environments (Matlab, Python, R, etc.). It provides a sophisticated compiler, high performance with numerical accuracy, and extensive mathematical function libraries. User-contributed packages are available for time series, statistics and machine learning, or operations research.
An introduction to the Julia language for scientific computing and its connection to Python with IJulia
Heidelberg Python Meetup Group, September 2014
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. IJulia, a collaboration between the IPython and Julia communities, provides a powerful browser-based graphical notebook interface to Julia and enables easy use of Python within Julia.
Presentation: Julia for Scientific Programming
Die Statistik-Umgebung R: Einführung, Übersicht, Anwendungen
VDI Arbeitskreis “Mess- und Automatisierungstechnik”“, Kassel, Februar 2014
Der Vortrag beinhaltet eine Einführung zu R, seiner Programmiersprache und seinen Entwicklungswerkzeugen. Beispiele vorhandener Methoden zu (nichtlinearen) Regression, zur Optimierung und zum Maschinellen Lernen (Data Mining). Der Vortrag richtet sich an alle, die an statistischer Datenverarbeitung und Visualisierung interessiert sind, bzw. sich über Fähigkeiten einer Open Source Software (OSS) in diesem Bereich informieren möchten.
Folien: Statistics Environment R (in English).
In PreparationSubgroup Discovery - a Data Mining Technique
Subgroup Discovery is a Data Mining algorithm that has high practical relevance in science and business applications. It identifies subsets of the data with "interesting" features. Subgroup Discovery is especially useful in the explanatory data analysis phase and can provide surprising insights into the data for you and/or your customers. Theory and two packages that implement this technique will be discussed.
Can R Be Used for Numerical Mathematics?
R is a "software environment for statistical computing and graphics." But can R be used in courses on Numerical Mathematics, and is it possible to apply R to real-world numerical problems? Many R packages will be discussed that provide functionality in numerical analysis and linear algebra, and ways to integrate with other scientific environments such as Python, Julia, MATLAB/Octave, or even Mathematica.
Symbolic and Numeric Differentiation With R
Function differentiation is an important technique for optimization problems and differential equations applications, in engineering and many other areas. The talk will discuss packages for symbolic differentiation, automatic forward differentiation implemented as S4 class, the complex step approach, central difference formulas, Richardson extrapolation for computing numerical Hessians, and higher order derivatives.
Functional Data Analysis
Not yet available
|Hans W. Borchers at mailbox dot org|