Course syllabus

Course PM

This page contains the program of the course: lectures and computer labs. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.

The schedule of the course is in TimeEdit.

Minutes of the mid-course meeting held on 24 September 2019.

Lectures

None of the lectures or 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 and notes
Wed 4 Sept Umberto Picchini

 

Wed 11 Sept Umberto Picchini More R, intro to LaTeX, more linear regression, underfitting/overfitting

 

Wed 18 Sept Umberto Picchini Bootstrap

 

 

Wed 25 Sept Petter Mostad Reliability and survival.

 

Wed 2 Oct Petter Mostad Bayesian decision theory.

 

 

Wed 9 Oct Annika Lang Intro to Python, Monte Carlo methods

 

 

Wed 16 Oct Annika Lang Simulation of stochastic processes

 

Monday 21 Oct Umberto Picchini

extra office hours: Umberto: 16.00-17.00

Wednesday 23 Oct Annika Lang

extra office hour:

Annika: 11.00-12.00

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Computer labs

Teaching assistants will be available for answering questions. There will not be any formal class.

Day Main topic
Thur 5 Sept A1
Mon 9 Sep A1
Thur 12 Sep A2 + possibly complete A1

Mon 16 Sep

A2

Thur 19 Sep

A2
Mon 23 Sep A2
Thur 26 Sep A3 (or finish A2)
Mon 30 Sep A3
Thur 3 Oct A3
Mon 7 Oct A4
Thur 10 Oct A4
Mon 14 Oct A5
Thur 17 Oct A5
Mon 21 Oct A6
Thur 24 Oct A6

 

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Course summary:

Date Details Due