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.

Notes of the midcourse meeting: Minutes of the mid-course meeting for TMS150-MSG400 2024-1.pdf

Lectures

See the course PM page for the "office hours" and how to interact with the lecturers.

(Here are the videos of the 2020 lectures. These were made at home during covid times, and are to be considered as useful material in case you miss a lecture, but we discourage you to rely on these videos as a substitute to attendance. Some of the topics have changed since, and we don't want you to get confused!)

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
Wed 4 Sept Umberto Picchini

 

Intro to the course; R; recapitulation on statistical inference for linear regression

 

lab0.pdf

demo.R

demo_cars.R

sleeptab.dat

slides_0_course-setup.pdf

slides_1.pdf

recaplinear.pdf

 

Wed 11 Sept Umberto Picchini model selection in linear regression using prediction error; transformation tools for the Y variable

 

demo_poly.R

demo_multiple.R

slides_2.pdf

model-choice.pdf

LaTeX report example

Guidelines report writing

tips_against_common_mistakes_and_plagiarism.pdf

Wed 18 Sept Umberto Picchini the bootstrap; a universal tool for uncertainty quantification

demo_matlab.m

demo_bootstrap.m

atlantic.txt

gametime.txt

 

slides_3.pdf

boot-notes.pdf

Matlab installation

Wed 25 Sept Ioanna Motschan-Armen/Annika Lang Intro to Python, Monte Carlo methods

 

Notes on lecture week 4.pdf

hello_world-1.py

normal_MC_error.py

normal_MC_error-1.py

 

TMS150_Monte_Carlo.pdf

TMS150-Project3_Monte_Carlo.pdf

Wed 2 Oct Annika Lang Simulation of stochastic processes

 

Slides on stochastic processes

Project 4 on stochastic processes

Wed 9 Oct
Moritz Schauer Bayesian inference 

 

bayesianmodelling-4.pdf
Wed 16 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.

QA = Question & Answers.

Presentations = these are dates when students can present to a teaching assistant (not to the class) their results, but only for assignments A1, A3 and A5.

It is not compulsory to present! You can always write a mini report too, but if you are confident you did things fairly ok, you can pass an assignment earlier via the presentation.

For each assignment A1-A3-A5 you can only present once! So better to present when you think you have done things fairly ok.

Please book dates via at the links below (first-come-first-served). Fill in your name for either the Monday *or* Thursday link, *not both*, at the time you want to present your solutions to the teaching assistants. One student only for each 10 minutes slot. Remember to be there in time. It is not possible to book multiple times.

If there occurs any problem with you attending at those dates for the presentations please contact Ioanna Motschan (ioannamo@chalmers.se).

 

Day 

 

Main topic

Doodle to sign in for presentation time slot

(If there occurs any problem with you attending at those dates for the presentations please contact Ioanna Motschan (ioannamo@chalmers.se).)

Thur 5 Sept at 8.00-11.45 A1 QA

 

Mon 9 Sept at 8.00-11.45 A1 QA and presentations

presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE 

Thur 12 Sept at 8.00-11.45 A2a QA and A1 presentations presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE 

Mon 16 Sept at 8.00-11.45

A2a QA

 

Thur 19 Sept at 8.00-11.45

A2b QA

 

Mon 23 Sept at 8.00-11.45

A2b QA

 

Thur 26 Sept at 8.00-11.45

A3 QA

 

Mon 30 Sept at 8.00-11.45 A3 QA  and presentations presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE
Thur 3 Oct at 8.00-11.45 A4 QA and A3 presentations

presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE

Mon 7 Oct at 8.00-11.45 A4 QA

 

Thur 10 Oct at 8.00-11.45 A5 QA

 

Mon 14 Oct at 8.00-11.45 A5 QA and presentations

presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE

Thur 17 Oct at 8.00-11.45 A6 QA and A5 presentations

presentations: 8:10-9:00 and 10:00-10:50 BOOK HERE

Mon 21 Oct at 8.00-11.45 A6 QA

 

Thur 24 Oct at 8.00-11.45 A1-A6 QA

 

 

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: 

 1 November

A1 R answers only (passed/not-passed) Wed 18 September @23.59  1 November
A2a R LaTeX report (with points) Wed 18 September @23.59  1 November
A2b Matlab LaTeX report (with points) Wed 25 September @23.59  1 November
A3 Python answers only (passed/not-passed) Wed 9 October @23.59  1 November
A4 Python LaTeX report (with points) Wed 9 October @23.59  1 November
A5 R answers only (passed/not-passed) Wed 23 October @23.59  1 November
A6 R LaTeX report (with points) Mon 28 October @23.59
 1 November

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Course summary:

Date Details Due