Gaussian Process Regression for Bayesian Machine Learning
4.4 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,676 students enrolled

Gaussian Process Regression for Bayesian Machine Learning

Introduction to a probabilistic modelling tool for modern machine learning, with fundamentals and application in Python
Bestseller
4.4 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,676 students enrolled
Created by Foster Lubbe
Last updated 6/2020
English
Current price: $11.99 Original price: $19.99 Discount: 40% off
2 days left at this price!
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This course includes
  • 1 hour on-demand video
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • The mathematics behind an algorithm such as the scikit-learn GaussianProcessRegressor algorithm
  • The benefits of Gaussian process regression
  • Examples of Gaussian process regression in action
  • The most important kernels needed for Gaussian process regression
  • How to apply Gaussian process regression in Python using scikit-learn
Requirements
  • A basic understanding of linear algebra
  • Basic experience with coding
Description

Probabilistic modelling, which falls under the Bayesian paradigm, is gaining popularity world-wide. Its powerful capabilities, such as giving a reliable estimation of its own uncertainty, makes Gaussian process regression a must-have skill for any data scientist. Gaussian process regression is especially powerful when applied in the fields of data science, financial analysis, engineering and geostatistics.

This course covers the fundamental mathematical concepts needed by the modern data scientist to confidently apply Gaussian process regression. The course also covers the implementation of Gaussian process regression in Python.

Who this course is for:
  • Data scientists, engineers and financial analysts looking to up their data analysis game
  • Anybody interested in probabilistic modelling and Bayesian statistics
Course content
Expand all 14 lectures 01:08:54
+ Fundamentals
5 lectures 28:03

All the answers to this quiz can be found in the resource attached to Lecture 1, labeled Reading 1. Do this quiz to test your knowledge!

Reading 1
3 questions

All the answers to this quiz can be found in the resource attached to Lecture 2, labeled Reading 2. Do this quiz to test your knowledge!

Reading 2
3 questions
An example of finding the conditional
03:21

All the answers to this quiz can be found in the resource attached to Lecture 3, labeled Reading 3. Do this quiz to test your knowledge!

Reading 3
2 questions
Supervised learning with the Gaussian process
09:11

All the answers to this quiz can be found in the resource attached to Lecture 4, labeled Reading 4. Do this quiz to test your knowledge!

Reading 4
2 questions
An example of supervised learning
03:55

All the answers to this quiz can be found in the resource attached to Lecture 5, labeled Reading 5. Do this quiz to test your knowledge!

Reading 5
3 questions
+ Application
9 lectures 39:05
Kernels and their usefulness
04:33

All the answers to this quiz can be found in the resource attached to Lecture 6, labeled Reading 6. Do this quiz to test your knowledge!

Reading 6
5 questions

Structure Discovery in Nonparametric Regression through Compositional Kernel Search:  https://arxiv.org/pdf/1302.4922.pdf

A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework: https://www.mdpi.com/1996-1073/11/4/935

The Kernel Cookbook: https://www.cs.toronto.edu/~duvenaud/cookbook/

Combining Kernels
07:30
Classic Gaussian process regression examples
04:00

All the answers to this quiz can be found in the resource attached to Lecture 7, labeled Reading 7. Do this quiz to test your knowledge!

Reading 7
3 questions
Scikit-learn
03:50
Scikit-learn GaussianProcessRegressor
03:28
Applying Gaussian process regression to real-world data II
03:33
Applying Gaussian process regression to real-world data III
02:53
+ Assignment
0 lectures 00:00
This assignment will help you to feel more confident about applying Gaussian process regression in Python and it will also allow the opportunity to play around with different kernels and optimizer settings. Code and a data file are provided that will allow you to implement Bayesian machine learning.
Running a Gaussian process regression algorithm in Python and optimizing kernels
1 question