Course syllabus
This page contains the program of the course: lectures and computer labs. The course is completely in-person, for both the lectures and the computer labs. There will be no zoom support sessions.
Many information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.
The schedule of the course is in TimeEdit.
Lectures
See the course PM page for the "office hours" and how to interact with the lecturers.
None of the computer lab sessions are mandatory to attend. However you are strongly encouraged to attend as we have limited resources to provide help outside teaching hours.
See the section Examination in the course PM for deadlines of mandatory hand-ins.
| Day | lecturer | Topics | code and data files |
slides/notes | Extras |
| Wed 3 Sept | Umberto Picchini |
Intro to the course; R; recapitulation on statistical inference for linear regression |
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| Wed 10 Sept | Umberto Picchini | model selection in linear regression using prediction error; transformation tools for the Y variable |
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| Wed 17 Sept | Umberto Picchini | the bootstrap; a universal tool for uncertainty quantification |
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| Wed 24 Sept | Annika Lang | Intro to Python, Monte Carlo methods |
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| Wed 1 Oct | Annika Lang | Simulation of stochastic processes |
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| Wed 8 Oct |
Moritz Schauer | Bayesian inference |
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| Wed 15 Oct | Moritz Schauer | Bayesian inference and decision theory |
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Computer labs
Computer labs will take place in the computer rooms MVF22/MVF24/MVF25 as from the schedule on TimeEdit. None of the computer lab sessions are mandatory to attend. However you are strongly encouraged to attend as we have limited resources to provide help outside teaching hours. More info are at the "Computer Labs" section at Course PM.
These labs are not structured with presentation of concepts from the teaching assistants. They are there to help if you have questions regarding exercises.
Deadlines for handing in
Here are the deadlines. For instructions on the assignments and info on the examination, see the course PM.
| Assignment | Language | Type of examination |
Recommended deadline |
Final deadline for all assignments: 2 November |
| A1 | R | LaTeX report | Wed 17 September @23.59 | 2 November |
| A2 | Matlab | LaTeX report | Wed 24 September @23.59 | 2 November |
| A3 | Python | LaTeX report | Wed 8 October @23.59 | 2 November |
| A4 | R | LaTeX report | Mon 27 October @23.59 | 2 November |
Course summary:
| Date | Details | Due |
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