Statistical Learning and Data Science

Korbinian Strimmer - Department of Mathematics
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Overview of teaching:

I teach mathematics and computer science students. My current module is "Statistics 2" (Year 2) for which you can find my lecture notes here:

• Statistics 2: Likelihood and Bayes (MATH27720)

Previously, I have also taught "Multivariate Statistics and Machine Learning" (Year 3, 6x Autumn 2018 to Autumn 2023) as well as "Statistical Methods" (Year 2, 5x Spring 2019 to Spring 2023) for which I wrote the following lecture notes that can be found here:

• Multivariate Statistics and Machine Learning (MATH38161)
• Statistical Methods: Likelihood, Bayes and Regression (MATH20802)

I also supervise BSc, MMath and MSc student projects in Statistics and related fields.

Please see below for the individual course home pages with time tables and links to further materials.

Current academic year (University of Manchester):

• 2024-2025:
  • Term 2: Module MATH27720 Statistics 2: Likelihood and Bayes
  • Project supervision (BSc and MSc)

Previous courses (University of Manchester):

• 2023-2024:
  • Term 2: Module MATH27720 Statistics 2: Likelihood and Bayes (395 students)
  • Term 1: Module MATH38161 Multivariate Statistics and Machine Learning (60 students)
  • Project supervision ( 2 BSc and 2 MSc)
• 2022-2023:
  • Term 2: Module MATH20802 Statistical Methods (320 students)
  • Term 2: Module MATH11712 Statistics 1 (tutorial / supervision class, 25 students)
  • Term 1: Module MATH38161 Multivariate Statistics and Machine Learning (70 students)
  • Project supervision (3 BSc, 3 MSc)
• 2021-2022:
  • Term 2: Module MATH20802 Statistical Methods (350 students)
  • Term 1: Module MATH38161 Multivariate Statistics and Machine Learning (75 students)
  • Project supervision (3 MSc, 3 BSc 3)
• 2020-2021:
  • Term 2: Module MATH20802 Statistical Methods (260 students)
  • Term 2: Statistics and Machine Learning Discussion Group
  • Term 1: Module MATH38161 Multivariate Statistics and Machine Learning (70 students)
  • Project supervision (5 MSc, 1 BSc)
• 2019-2020:
  • Term 2: Module MATH20802 Statistical Methods (250 students)
  • Term 1: Module MATH20802 Multivariate Statistics and Machine Learning (90 students)
  • Project supervision (3 MSc, 1 MMath, 2 BSc)
• 2018-2019:
  • Term 2: Module MATH20802 Statistical Methods (270 students)
  • Term 1: Module MATH38161 Multivariate Statistics and Machine Learning (70 students)
  • Project supervision (4 MSc, 1 MMath, 2 BSc)
• 2017-2018:
  • Term 2: Module MATH1982 Foundations in Probability (310 students)
  • Term 2: Statistics and Machine Learning Discussion Group
  • Project supervision (4 MSc)

Previous courses (Imperial College London):

• 2017-2018:
  • Term 2: Module Advanced Regression, shared (M.Sc.)
• 2016-2017:
  • Term 2: Module Advanced Regression (M.Sc.)
  • All terms: Seminar Current Topics in Biostatistics
• 2015-2016:
  • Term 2: Module Advanced Regression (M.Sc.)
  • All terms: Seminar Current Topics in Biostatistics
• 2014-2015:
  • Term 2: Module Advanced Regression, shared (M.Sc.)
  • Term 1: Tutorials Introduction to Statistical Thinking and Data Analysis
  • All terms: Seminar Current Topics in Biostatistics

Previous courses (University of Leipzig):

• 2013-2014:
  • SS 2014: Grundbegriffe der medizinischen Biometrie und Epidemiologie
  • WS 2013: Statistisches Lernen
  • WS 2013: D. V. Lindley - Understanding Uncertainty
• 2012-2013:
  • SS 2013: Grundbegriffe der medizinischen Biometrie und Epidemiologie
  • WS 2012: Grundlagen des Maschinellen Lernens und der Statistik
  • WS 2012: Einführung in R
• 2011-2012:
  • SS 2012: Grundbegriffe der medizinischen Biometrie und Epidemiologie
  • SS 2012: Statistical Analysis of RNA-Seq Data
  • WS 2011: E.T. Jaynes: Probability Theory - The Logic of Science (ctd.)
  • WS 2011: Einführung in R
  • WS 2011 + SS 2012: Research colloquium ("F-Seminar")
• 2010-2011:
  • SS 2011: Grundbegriffe der medizinischen Biometrie und Epidemiologie
  • SS 2011: E.T. Jaynes: Probability Theory - The Logic of Science
  • WS 2010: Grundlagen des Maschinellen Lernens und der Statistik
  • WS 2010 + SS 2011: Research colloquium ("F-Seminar")
  • WS 2010: Einführung in R
• 2009-2010:
  • SS 2010: Grundbegriffe der medizinischen Biometrie und Epidemiologie
  • WS 2009: Grundlagen des Maschinellen Lernens und der Statistik
  • WS 2009 + SS 2010: Research colloquium ("F-Seminar")
  • WS 2009: Einführung in R
• 2008-2009:
  • SS 2009: Einführung in die medizinische Biometrie und Epidemiologie
  • SS 2009: Current topics in biostatistics
  • WS 2008: Grundlagen des Maschinellen Lernens und der Statistik
  • WS 2008 + SS 2009: Research colloquium ("F-Seminar")
• 2007-2008:
  • SS 2008: Einführung in die medizinische Biometrie und Epidemiologie
  • SS 2008: Research colloquium ("F-Seminar")
  • WS 2007: Grundlagen des Maschinellen Lernens und der Statistik
• 2006-2007:
  • SS 2007: Statistical Modeling of Biological Networks
  • SS 2007: Concepts of Causality in Biology and Medicine
  • SS 2007: Current Research in Statistics and Bioinformatics

Previous courses (University of Munich):

• 2005-2006:
  • SS 2006: Modeling, Simulation and Inference of Complex Biological Systems
• 2004-2005:
  • SS 2005: Strategies for Analyzing High-Dimensional Data (literature list)
  • SS 2005: Journal club on statistics in genomics and proteomics
  • WS 2004: Statistical Analysis of Gene Expression Data
• 2003-2004:
  • SS 2004: Stochastic Modeling of Complex Biological Systems
  • WS 2003: Practical Analysis of Gene Expression Data
• 2002-2003:
  • SS 2003: Genetic Networks: Models and Estimation
  • WS 2002: Introduction to Statistical Models in Sequence Analysis
  • WS 2002: Book and journal club on molecular evolution
• 2001-2002:
  • SS 2002: Statistical Analysis of Gene Expression Data

Last modified: August 18, 2023