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Showing episodes and shows of

Philipp Packmohr

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Data Science Phil

Data Science Phil The Fermi ProblemIn this episode I talk to Bence Mélykúti, DPhil. Bence has an MSc degree (incl. undergrad studies) in mathematics from the Mathematical Institute of the Eötvös Loránd University (ELTE), Budapest, Hungary. He defended his doctoral thesis in January 2011 at the University of Oxford, UK, where he was with the Department of Statistics and the Life Sciences Interface Doctoral Training Centre as a member of Keble College. He worked under the supervision of Prof. Alison Etheridge (Dept. of Statistics) and Dr. Antonis Papachristodoulou (Control Group, Dept. of Engineering Science). His Research interests include: The interfaces of mathemat...

2019-09-241h 26

Data Science Phil

Data Science Phil The case-crossover design via penalized regressionIn this episode I talk to Sam Doerken. Sam is a mathematician by training, having studied mathematics at the University of Heidelberg. He did his diploma thesis in mathematics on Probabilistic Forecasting of U.S. Treasury Bills . Since 2012 he works at the Institute of Medical Biometry and Statistics at the University of Freiburg. In this part we cover the paper "The case-crossover design via penalized regression" , published in BMC Medical Research Methodology. The authors conclude that "for the case-crossover design, we also encourage penalized regression for routine use."

2019-09-1713 min

Data Science Phil

Data Science Phil Probabilistic Forecasting of U.S. Treasury BillsIn this episode I talk to Sam Doerken. Sam is a mathematician by training, having studied mathematics at the University of Heidelberg. He did his diploma thesis in mathematics on Probabilistic Forecasting of U.S. Treasury Bills . Since 2012 he works at the Institute of Medical Biometry and Statistics at the University of Freiburg. In this episode we talk abot the topic of his diploma thesis Probabilistic Forecasting of U.S. Treasury Bills and we cover time series analysis.

2019-09-1014 min

Data Science Phil

Data Science Phil Theory of Distributed ComputationIn this episode I talk to Philipp Schneider, PhD student in theoretical computer science in Fabian Kuhns group at the University of Freiburg. Before that he studied computer science at the Karlsruhe Institute of Technology. Philipps reseach is concerned with the Theory of Distributed Computation. In distributed computation one assumes processors (or computation nodes) that are far away from each other. The input of some problem is distributed on these computing nodes and they have to collaborate to compute the solutions. The goal is to optimize the rescources, which in this case is communication, specifically the number...

2019-08-2641 min

Data Science Phil

Data Science Phil Artificial Neural NetworksDr. Sebastian Ritterbusch and me talk about neuronal networks at #GPN19 in Karlsruhe, Germany.

2019-08-251h 19

Data Science Phil

Data Science Phil Open problems in mathematicsIn this episode I talk to Gaetan Leclerc about some open problems in mathematics, part of the so called Millenium problems. Gaeton is a first year master student at the ENS Rennes. Before that he studied mathematics at the École Normale Supérieure de Rennes (ENS Rennes). We cover the Navier-Stokes equations which are very important in computational fluid dynamics and thus have many applications in engineering, medicine, biology and climate science. The mathematician Grigori Perelman presented a proof of the Poincaré conjecture in three papers made available in 2002 and 2003 on the arXiv. We also talk about the P vs NP p...

2019-08-1032 min

Modellansatz

Modellansatz Propensity Score MatchingAuf der Gulaschprogrammiernacht 2019 traf Sebastian auf den Podcaster Data Science Phil Philipp Packmohr @PPackmohr. Sein Interesse zur Data Science entstand während seines Studiums in den Life Sciences an der Hochschule Furtwangen in den Bereichen der molekularen und technischen Medizin und zu Medical Diagnostic Technologies. In seiner Masterarbeit hat er sich betreut von Prof. Dr. Matthias Kohl mit der statistischen Aufbereitung von Beobachtungsstudien befasst, genauer mit der kausalen Inferenz aus Observationsdaten mit Propensity Score Matching Algorithmen. Kausale Inferenz, das Schließen von Beobachtungen auf kausale Zusammenhänge, ist tatsächlich sehr wichtig in allen empirischen Wissenschaften wie zum...

2019-06-131h 09

Data Science Phil

Data Science Phil Statistical significanceThoughts about a commentary by Amrhein et al. In Nature .

2019-04-1004 min

Data Science Phil

Data Science Phil Start-up summitStuttgart

2019-02-0104 min

Data Science Phil

Data Science Phil OptimizationOptimization

2018-12-2903 min

Data Science Phil

Data Science Phil Xtensor with Wolf VollprechtInterview with Wolf Vollprecht at #PyConDE

2018-10-2903 min

Data Science Phil

Data Science Phil Python in engineering with Nico LiebersInterview with Dr.Ing. Nico Liebers at PyConDE, the German Pycon.

2018-10-2905 min

Data Science Phil

Data Science Phil Weather forecasting with Lena VolzhinaInterview with Lena Volzhina at #PyConDE, the German Pycon.

2018-10-2906 min

Data Science Phil

Data Science Phil Chromebook Data ScienceCheck out Jeff Leeks chromebook data science course !

2018-10-0605 min

Data Science Phil

Data Science Phil RegressionIn this episode we talk about regression.

2018-09-2507 min

Data Science Phil

Data Science Phil Statistical EvaluationIn this episode we talk about statistical Evaluation.

2018-09-2540 min

Data Science Phil

Data Science Phil Estimation and estimatorsIn this episode we talk about estimators.

2018-09-2543 min

Data Science Phil

Data Science Phil Probability distributionsIn this episode we cover probability distributions.

2018-09-1125 min

Data Science Phil

Data Science Phil ProbabilityIn this episode we talk about #probability #theory .

2018-09-0631 min

Data Science Phil

Data Science Phil Descriptive StatisticsIn this episode we are talking about #descrptive #statistics

2018-09-0446 min

Data Science Phil

Data Science Phil The R languageIn this episode we talk about R

2018-09-0105 min

Data Science Phil

Data Science Phil IntroductionThis is the Introduction to the podcast

2018-09-0108 min