CS-E5710 - Bayesian Data Analysis D, Lecture, 4.9.2023-8.12.2023

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

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

Credits: 5

Schedule: 04.09.2023 - 08.12.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Aki Vehtari

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

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

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Assignments (67%) and a final project work with presentation (33%). Minimum of 50% of points must be obtained from both the assignments and the project work.

Workload

  • valid for whole curriculum period:

    Lectures 10x2h, computer exercises 10x2h, independent studying (text book, programming, home assignment and project reports), project presentation

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    1 No Poverty

    2 Zero Hunger

    3 Good Health and Well-being

    4 Quality Education

    5 Gender Equality

    6 Clean Water and Sanitation

    7 Affordable and Clean Energy

    8 Decent Work and Economic Growth

    9 Industry, Innovation and Infrastructure

    10 Reduced Inequality

    11 Sustainable Cities and Communities

    12 Responsible Production and Consumption

    13 Climate Action

    14 Life Below Water

    15 Life on Land

    16 Peace and Justice Strong Institutions

    17 Partnerships for the Goals

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn I - II
    2023-2024 Autumn I - II

    Enrollment :

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

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