Language: | Slovenian |
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Year of publishing: | 2019 |
Typology: | 2.11 - Undergraduate Thesis |
Organization: | UL FS - Faculty of Mechanical Engineering |
Publisher: | [S. Kovačič] |
UDC: | 519.2 |
COBISS: | 18737241 |
Views: | 1395 |
Downloads: | 264 |
Average score: | 0 (0 votes) |
Metadata: |
Secondary language: | English |
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Secondary title: | Gaussian process regression |
Secondary abstract: | The thesis presents the Gaussian process regression from the weight space view and the function space view. It examines some of Bayesian statistics and normal distribution properties. For modeling and machine learning purposes the model learning theory is also presented. Since covariance functions are tightly connected to the Gaussian process the thesis contains a presentation of the most frequent covariance functions. The empirical part of the thesis includes a description of Python’s Scikit-learn machine learning library as well as an example of the Gaussian process regression based on the results of the 2019 national assessment of elementary school students in Slovenia. |
Secondary keywords: | mathematics;Gaussian processes;covariance functions;regression;machine learning; |
Type (COBISS): | Final seminar paper |
Study programme: | 0 |
Embargo end date (OpenAIRE): | 1970-01-01 |
Thesis comment: | Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 1. stopnja |
Pages: | 28 str. |
ID: | 11229752 |