Udemy
MOOC / Non-credit
0
Theory of Gaussian Process Regression for Machine Learning
About this course
The course walks through the core mathematics behind Gaussian process regression, covering kernels, covariance functions, and Bayesian inference. It then shows how to translate those concepts into working Python code using common libraries. By the end, you’ll be able to build GP models, tune their hyper‑parameters, and interpret the uncertainty estimates they provide for real‑world data.
C
55/100
CourseAsk score
- What the provider tells you
- 31/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Explain the theoretical foundations of Gaussian process regression
- Identify and construct appropriate kernel and covariance functions
- Implement Gaussian process regression models in Python
- Tune hyper‑parameters and assess model uncertainty
Course objectives
- Derive GP regression equations from Bayesian principles
- Translate mathematical concepts into Python implementations
- Apply GP models to datasets from finance, engineering, or geostatistics
Machine Learning
Statistics & Probability
#python
#regression
#data science
#financial analysis
#engineering
#bayesian methods
#probabilistic modeling
#geostatistics
#uncertainty estimation
#Gaussian processes
#gaussian process regression
#bayesian inference
#probabilistic modelling
#uncertainty quantification
#kernel functions
#covariance functions
#hyperparameter tuning
#scikit-learn
#numpy
#engineering applications
$24.99
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