Administrative information
| Title |
Linear Regression |
 |
| Duration |
60 |
| Module |
A |
| Lesson Type |
Lecture |
| Focus |
Practical - AI Modelling |
| Topic |
Linear Regression |
Keywords
linear regression, maximum likelihood, maximum a posteriori, basis functions,
Learning Goals
- To acquire demonstrable knowledge of what linear regression is
- To acquire demonstrable knowledge of the various approaches to linear regression: maximum likelihood estimation (MLE), maximum a-posteriori estimation (MAP), Bayesian
- To acquire demonstrable knowledge of analytical closed form for fitting a linear regression model
- To acquire demonstrable knowledge of non-linearizing linear models
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- A review of basic linear algebra and solving linear systems numerically.
Optional for Students
None.
References and background for students:
- Bishop, Christopher M. (2006). Pattern recognition and machine learning, Chapter 3.
Recommended for Teachers
- Familiarize themselves with the demonstration material.
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
Cover the topics in the lesson outline and demonstrate the concepts using the interactive notebooks (fitting a model "manually", demonstrating the effects of the hyperparameters). Give a brief overview of the code.
Outline/time schedule
| Duration (min) |
Description |
Concepts |
| 5 |
Introduction to linear regression |
hyperplane, normal, bias |
| 5 |
Defining a linear regression model |
additive noise, Gaussian distribution |
| 15 |
Maximum likelihood estimation |
squared error, linear solvers |
| 10 |
Nonlinear (polynomial) regression |
polynomial regression, transformation of samples |
| 10 |
Maximum a posteriori estimation |
hyperparameter, prior, regularization, numerical stability |
| 5 |
Bayesian linear regression |
posterior, uncertainty, predictive mean and variance |
| 10 |
Demonstration |
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More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
 |
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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