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Bayesian data analysis

Individual course

Max amount of FITech students: 100

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Mastering the prerequisite skills is very important in order to complete this course.

Course contents

  • Bayesian probability theory and bayesian inference
  • Bayesian models and their analysis
  • Computational methods, Markov-Chain Monte Carlo

Learning outcomes

After the course, the student can

  • explain the central concepts in Bayesian statistics, and name steps of the Bayesian modeling process.
  • recognize usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations.
  • compare the most popular Bayesian simulation methods, and implement them.
  • use analytic and simulation based methods for learning the parameters of a given model.
  • estimate the fit of a model to data and compare models.

Teaching schedule

It is possible to take the course fully online.

Lectures will be organized on Mondays at 14:15-16:00 (recording is available after the lecture). Exercise sessions (Otaniemi) will be held on Wednesdays, Thursdays and Fridays. 

More information on Aalto University’s course page.

You can get a digital badge after completing this course.

Responsible teacher

Aalto University
Aki Vehtari

Further information about the studies

Aalto University
FITech ICT contact person

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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Category:
ICT Studies
Topic:
AI and machine learning
Course code:
CS-E5710
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
12.9.–9.12.2022
Application deadline:
4.9.2022
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Blended,
Online
Place of contact learning:
Espoo
Language:
English
General prerequisites:
Differential and integral calculus, basics of probability and statistics, basics of programming (R or Python). Recommended: matrix algebra.
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