Teaching
Since acquiring my master degree (cand. scient.) I've taught the courses
listed below.
Lectures given at specific occations can be found
here.
Students that I've supervised and their projects can be found
here.
Teachers: Troels C. Petersen along with Daniel Murnane.
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2024.
Prerequisites: Mathematics, programming (python), and independence.
Text book: Slides and links along with
Applied
Machine Learning by David Forsyth.
Outline: Graduate course giving students insight in and experience with Machine Learning.
Key words: Tree-based methods, neural networks, dimensionality reduction, loss function, hyper parameter optimisation, etc..
Number of students: 72
Teacher: Troels C. Petersen (NBI HEP) along with Mathias Heltberg (NBI).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2023 - January 2024.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 102
Teachers: Troels C. Petersen along with Charles Steinhardt.
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2023.
Prerequisites: Mathematics, programming (python), and independence.
Text book: Slides and links along with
Applied
Machine Learning by David Forsyth.
Outline: Graduate course giving students insight in and experience with Machine Learning.
Key words: Tree-based methods, neural networks, dimensionality reduction, loss function, hyper parameter optimisation, etc..
Number of students: 98
Teacher: Troels C. Petersen (NBI HEP) along with Mathias Heltberg (NBI).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2022 - January 2023.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 140
Teachers: Troels C. Petersen along with Charles Steinhardt and Julius Kirkegaard.
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2022.
Prerequisites: Mathematics, programming (python), and independence.
Text book: Slides and links along with The Elements of Statistical Learning II
by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Outline: Graduate course giving students insight in and experience with Machine Learning.
Key words: Tree-based methods, neural networks, dimensionality reduction, loss function, hyper parameter optimisation, etc..
Number of students: 101
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2021 - January 2022.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 145
Teachers: Troels C. Petersen and Adriano Agnello..
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2021.
Prerequisites: Mathematics, programming (python), and independence.
Text book: The Elements of Statistical Learning II
by Trevor Hastie, Robert Tibshirani, Jerome Friedman, along with
two other books, notes, papers, and links.
Outline: Graduate course giving students insight in and experience with Machine Learning.
Key words: Dimensionality reduction, cost function, tree-based ML methods, neural networks, hyper parameter optimisation.
Number of students: 103
Applied Statistics
(Winter 2020-2021, last five weeks online due to Covid-19)
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2020 - January 2021.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 150
Introduction to Statistics (iNano at AU)
(January 2021, online due to Covid-19)
Teacher: Troels C. Petersen (NBI HEP).
Period: January 2021.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Nano-science Ph.D. statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 21
Teachers: Troels C. Petersen, Brian Vinter, and Adriano Agnello..
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2020.
Prerequisites: Mathematics, programming (python), and independence.
Text book: The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman, along with
two other books, notes, papers, and links.
Outline: Graduate course giving students insight and experience into using machine learning.
Key words: Dimensionality reduction, cost function, tree-based ML methods, neural networks, hyper parameter optimisation.
Number of students: 58
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2019 - January 2020.
Prerequisites: Simple mathematics (calculus and linear algebra) and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo, Hypothesis Testing.
Number of students: 109
Teachers: Troels C. Petersen, Brian Vinter, Joachim Mathiesen, and Adriano Agnello..
Lectures and problem solving: Monday 13-17 and Wednesday 08-17 (Skemagruppe C).
Period: April - June 2019.
Prerequisites: Mathematics, programming (python), and independence.
Text book: The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman, along with
two other books, notes, papers, and links.
Outline: Graduate course giving students insight and experience into using machine learning.
Key words: Dimensionality reduction, cost function, tree-based ML methods, neural networks, hyper parameter optimisation.
Number of students: 48
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2018 - January 2019.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Number of students: 128
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2017 - January 2018.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2016 - January 2017.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: November 2015 - January 2016.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: September - October 2014.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: September - October 2013.
Prerequisites: Simple mathematics and programming (python).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students experience in data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: September - October 2012.
Prerequisites: Simple mathematics and programming (C++).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students methods for data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: September - October 2011.
Prerequisites: Simple mathematics and programming (C++).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students methods for data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Fitting, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 9-12, Tuesday 13-17, and Friday 9-12 (Skemagruppe B).
Period: September - October 2010.
Prerequisites: Simple mathematics and programming (C++).
Text book: Statistics: A guide to the use of statistics by Roger Barlow.
Outline: Graduate statistics course giving students methods for data analysis.
Key words: PDF, Correlation, Estimators, Chi-Square, Likelihood, Monte Carlo.
Teacher: Troels C. Petersen (NBI HEP).
Lectures and problem solving: Monday 8-12, Tuesday 13-17, and Friday 8-12 (Skemagruppe B).
Period: September - October 2009.
Prerequisites: Simple mathematics and programming (C++).
Text book: Statistical
Data Analysis by Glen Cowan.
Outline: Graduate statistics course giving students methods for data analysis.
Key words: PDF, Correlation, Chi-Square, Likelihood, Monte Carlo.
Class Teacher: Troels C. Petersen (NBI HEP).
Problem solving: Tuesday 8-10 and Friday 8-10 in HCØ A102.
Period: September - November 2005.
Prerequisites: None.
Text book: Elements of Newtonian Mechanics by J. M. Knudsen and
P. G. Hjorth.
Outline: Undergraduate physics course giving students an
introduction to Newtonian Mechanics.
Key words: Newton's Laws, Celestial Mechanics, Friction, Rotation.
Teachers: Jørn Dines Hansen, Peter Hansen and Troels C. Petersen (NBI HEP).
Lectures: Monday and Wednesday 9-12 in NBI Auditorium M.
Problem solving: Wednesday 13-17 in NBI Auditorium M.
Period: November 2004 - February 2005.
Prerequisites: Simple programming (Fortran or C++).
Text book: "Techniques for Nuclear and Particle Physics Experiments: A
How-To Approach" by W.R. Leo and notes.
Outline: Graduate course on detectors and experimental methods in particle physics.
Key words: Bethe-Block formula, Tracking, Calorimetry, Radiation, Data Analysis.
Teacher: Troels C. Petersen (NBI HEP).
Lectures: Wednesday 13-14 in NBI Auditorium M.
Problem solving: Wednesday 14-16 in NBI computer rooms (Kk2).
Period: February - May 2001.
Prerequisites: Simple programming (Fortran or C++).
Text book: Statistical
Data Analysis by Glen Cowan.
Outline: Graduate statistics course giving students methods for data analysis.
Key words: PDF, Correlation, Chi-Square, Likelihood, Monte Carlo.