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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
aki.vehtari(at)aalto.fi
Further information about the studies
Aalto University
FITech ICT contact person
fitech-sci(at)aalto.fi
Contact person for applications
FITech Network University
Fanny Qvickström, Student services specialist
info(at)fitech.io
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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
Degree student
Teaching methods:
Blended,
Online
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.