Master's Course, SS2015 Faculty of Physics and Astronomy, University of Heidelberg
Course LSF entry
Lecturer: PD Dr. Coryn Bailer-Jones
Assistant: Dr. Morgan Fouesneau
Summer semester 2015
This course will provide an introduction to using statistics and computational methods to analyse data. It comprises one 2-hour lecture per week, plus one 2-hour exercise session per week during which you will put into practice what you have learned in the lectures. There will also be homework assignments.
This course will take a pragmatic approach. The focus will be on concepts, understanding problems, and the application of techniques to solving problems, rather than reproducing proofs or teaching you recipes to memorize.
the Course website is available here
This repository gives the homeworks related datasets and eventually corrections
The repository will be updated after each class to give the assignments. All datasets, gists of code will also be included.
Examples of solutions (hardly unique) will be included eventually.
Notebooks have no meaning of imposing a format to give us back your homework. It only gives me a convenient way to keep both texts and codes at the same place.
The assignment for next week is to finish the exploration of the main routines/functions of the language of your choice and to also give the solutions to the exercises: both plotting/exploration and exercises parts are expected for next week.
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We do not mark your coding skills, any language is possible, take the one that is convenient and efficient.
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This means we do not read the codes. We do not look out for comments in the codes, but we will not guess what a plot means. Be explicit and describe even in once sentence what you did.
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Feel free to use the notebooks (it may not be the most efficient), be careful when printing (Check out nbconvert to produce a pdf or even latex document)
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We do not impose the language. Feel free to use any that you judge efficient for you. Obviously we cannot provide full support, nor we cannot give full tutorials.
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If you use R, many examples of code will be included in the lecture notes. If you use Python, all the exercises will be using python (when coding is required).
In case you cannot/do not want to install libraries or softwares on your computer, some free online services exist, such as:
Sage Cloud: python, R, and other languages
Wakari Python only.
some libraries that you may find useful later depending on your language.
There will be 12 lectures on the following dates (the exercise session is on the following day). The topics allocated to the dates may well change!
| Lecture date | Topic | Exercises |
|---|---|---|
| 14 April | Introduction and probability basics | notebook rvs.dat |
| 21 April | Estimation and error: describing data and distributions | notebook star.csv |
| 28 April | Statistical models and inference | |
| 5 May | Linear models and regression | |
| 12 May | (Bayesian) Model fitting | |
| 19 May | Monte Carlo methods | |
| 26 May | Model comparison and selection | |
| 2 June | No lecture | |
| 9 June | Model complexity | |
| 16 June | Likelihood and optimization | |
| 23 June | Nonparametric methods | |
| 30 June | Something else (details TBD) | |
| 7 July | Gaussian processes | |
| 14 July | Study week | |
| 21 July | Exam (maybe; date to be decided with the participants) |