CS-E4895 - Gaussian Processes D, Lecture, 27.2.2023-13.4.2023

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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

The course gives a comprehensive overview of Gaussian processes (GPs) in machine learning. After the course, the student can recognize where GPs can be used and know their limitations as well as their theoretical underpinnings. The student is familiar with the most common approximative inference methods for GPs and is familiar with their contemporary use and applications.

Credits: 5

Schedule: 27.02.2023 - 13.04.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Arno Solin

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:

    Gaussian processes (GPs) are a powerful machine learning paradigm for Bayesian nonparametric modelling. This course will give an overview of Gaussian processes in machine learning, and it provides both a theoretical and practical background for leveraging them. The course covers Gaussian process regression, classification, unsupervised modelling, as well as a selection of more recent specialised topics. This is a more specialized course and assumes students already have a background in machine learning.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Solving/returning assignments.

Workload

  • valid for whole curriculum period:

    Contact teaching c. 36 h, assignments related to course content c. 40 h, independent work c. 60 h.

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring IV - V
    2023-2024 Spring IV - V

    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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