Kaisa_2012_3_photo by Veikko Somerpuro

Anmäl dig
14.8.2017 kl. 08:00 - 1.10.2017 kl. 23:59
Logga in för att se kursnyckeln för Moodle.


Bild för Antti Honkela

Antti Honkela

Publicerad, 23.1.2018 kl. 15:46

The self-study version of Computational statistics I for spring 2018 is now available.
Please find the course page at https://courses.helsinki.fi/en/mast32001/123283812 and register in WebOodi if you are interested.

The self-study course will consist of similar weekly exercise problems as in autumn 2017, done at your own pace. The deadline for returning all the exercises will be in May. There will also be a home exam in May. You can use either the exercise points from the autumn or spring (whichever are higher) in the exam.

Bild för Antti Honkela

Antti Honkela

Publicerad, 18.10.2017 kl. 9:33

Another opportunity for completing Computational Statistics I in the spring 2018

There will be an additional self-study version of Computational Statistics I offered in the spring, especially for students who have not been able to follow the course during the autumn.

The self-study version will include similar assignments as the regular course: 7 sets of "weekly" exercises and a home exam. The "weekly" exercises can be completed at your own pace before a single deadline in late spring while the home exam will be at a specific time in late spring. The exact deadlines will be announced later. There will be no contact teaching for the self-study course.

Weekly exercise points collected during the autumn 2017 course will be valid for the home exam in spring 2018.


I den här delen hittar du kursens tidsschema. Kontrollera eventuella andra tider i beskrivning.

ons 6.9.2017
14:15 - 16:00
tors 7.9.2017
12:15 - 14:00
fre 8.9.2017
12:15 - 14:00
ons 13.9.2017
14:15 - 16:00
tors 14.9.2017
12:15 - 14:00
fre 15.9.2017
12:15 - 14:00
ons 20.9.2017
14:15 - 16:00
tors 21.9.2017
12:15 - 14:00
fre 22.9.2017
12:15 - 14:00
ons 27.9.2017
14:15 - 16:00
tors 28.9.2017
12:15 - 14:00
fre 29.9.2017
12:15 - 14:00
ons 4.10.2017
14:15 - 16:00
tors 5.10.2017
12:15 - 14:00
fre 6.10.2017
12:15 - 14:00
ons 11.10.2017
14:15 - 16:00
tors 12.10.2017
12:15 - 14:00
fre 13.10.2017
12:15 - 14:00
ons 18.10.2017
14:15 - 16:00
tors 19.10.2017
12:15 - 14:00
fre 20.10.2017
12:15 - 14:00


The course will be evaluated based on weekly computer exercises returned to Moodle (40% of the grade) and a home exam (60% of the grade).

The weekly course schedule will consist of interactive lectures with small computer programming tasks on Wednesday and Thursday. The Friday session will be a workshop for working on the weekly computer exercises.

There is no textbook, so attendance at the interactive lectures is highly recommended.


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.