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13.8.2018 at 08:00 - 19.10.2018 at 23:59


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Antti Honkela's picture

Antti Honkela

Published, 3.9.2018 at 12:13

Thank you for registering to the course Computational statistics I!

The course has attracted far more students than can comfortably fit to our regular lecture room (computer lab), so we have some special arrangements for the first lecture.


The FIRST LECTURE WILL START IN LECTURE HALL B123 in Exactum on Tuesday 4 September at 10:15. We will split up to other rooms for more interactive work after a brief introduction.


All course sessions will include plenty of computer-based work. Because of the popularity of the course, there will not be enough seats for all in the computer lab, so PLEASE BRING YOUR OWN LAPTOP, if possible.

PLEASE MAKE SURE YOU HAVE THE NECESSARY SOFTWARE INSTALLED ON YOUR LAPTOP BEFORE JOINING THE FIRST SESSION. It is highly recommended to use Python programming language during the course, because we will make use of some features that are not available in R, for example.

Required Python software:
Python, version >=3.5
IPython / Jupyter notebook

Please see the installation instructions for more help:

The easiest options are to install using your OS package manager (for Linux) or using Anaconda.

Best regards,


P.S. If you have the time, it is a good idea to take a look at Chapter 1 of the course notes and the computer exercises for Lecture 1, both available on the course Moodle page (link available on the course web page https://courses.helsinki.fi/fi/mast32001/124789436).


Here is the course’s teaching schedule. Check the description for possible other schedules.

Tue 4.9.2018
10:15 - 12:00
Thu 6.9.2018
12:15 - 14:00
Fri 7.9.2018
12:15 - 14:00
Tue 11.9.2018
10:15 - 12:00
Thu 13.9.2018
12:15 - 14:00
Fri 14.9.2018
12:15 - 14:00
Tue 18.9.2018
10:15 - 12:00
Thu 20.9.2018
12:15 - 14:00
Fri 21.9.2018
12:15 - 14:00
Tue 25.9.2018
10:15 - 12:00
Thu 27.9.2018
12:15 - 14:00
Fri 28.9.2018
12:15 - 14:00
Tue 2.10.2018
10:15 - 12:00
Thu 4.10.2018
12:15 - 14:00
Fri 5.10.2018
12:15 - 14:00
Tue 9.10.2018
10:15 - 12:00
Thu 11.10.2018
12:15 - 14:00
Fri 12.10.2018
12:15 - 14:00
Tue 16.10.2018
10:15 - 12:00
Thu 18.10.2018
12:15 - 14:00
Fri 19.10.2018
12:15 - 14:00

Conduct of the course

The course will be evaluated based on weekly computer exercises returned to Moodle (50% of the grade) and a home exam (50% of the grade).

After the opening session on Tuesday 4 September, the weekly course schedule will consist of interactive lectures with small computer programming tasks on Thursday and Friday. The Tuesday session will be a workshop for working on the weekly computer exercises, the deadline for which will be on Wednesday.

There is no textbook, but lecture notes covering the course contents will be provided.


The course is compulsory for students of the Statistics study track in the Master's Programme in Mathematics and Statistics.

Master's Programme in Mathematics and Statistics is responsible for the course.

The course belongs to the Statistics and Social statistics module in the Master's Programme in Mathematics and Statistics.

The course also belongs to the Machine Learning and Statistical Data Science modules in the Master's Programme in Data Science.

The course is available to students from other degree programmes.

BSc courses on linear algebra, probability calculus, statistical inference; basic programming skills

Fundamentals of differential equations

Knowledge and use of common general computational tools to perform reliable statistical analyses. Understanding the theoretical foundations of the most important Monte Carlo methods. Applying and implementing computational statistical procedures on a high-level programming language.

Recommended time/stage of studies for completion: 1. or 2. year of MSc studies.

Term/teaching period when the course will be offered: yearly during the autumn term, period I.

Most important numerical and computational methods and principles for statistics. Theory and practice of methods for sampling from probability distributions including rejection sampling, importance sampling, generic Markov chain Monte Carlo and Hamiltonian Monte Carlo. Overview of modern methods for approximate inference. The computer projects can be implemented in Python (preferred) or R.

Exercises and home exam.

Lecture notes and articles to be announced during the course

Lectures, exercises, computer exercises, project work

Computer excercises and a computer-based home exam, Course will be graded with grades 1-5

Exercises and project work.