Administrative information

Title	Linear Regression
Duration	60
Module	A
Lesson Type	Practical
Focus	Practical - AI Modelling
Topic	AI modelling

Keywords

linear regression, maximum likelihood, maximum a posteriori, basis functions,

Learning Goals

To be able to fit a linear regression model using various estimation approaches and fitting methods with the use of Python and suitable packages
To be able to use efficient matrix operations in linear regression

Expected Preparation

Learning Events to be Completed Before

Lecture: Linear Regression

Obligatory for Students

A review of basic linear algebra and solving linear systems numerically.

Optional for Students

None.

References and background for students:

Scikit-learn documentation and examples: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

Recommended for Teachers

A review of how the numpy functions used in the notebook are parameterized.

Lesson Materials

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

This learning event consist of laboratory tasks that shall be solved by the students with the help of the leading instructor.

Prepare a notebook environment with numpy, matplotlib and scikit-learn installed.

Outline/time schedule


Duration (min)	Description	Concepts
5	Brief of the tasks to be conducted
5	Generating linear datasets with Gaussian noise	additive noise, np.random.randn()
15	Fitting and evaluating linear regression models in via linear solvers	matrix operations in numpy, np.linalg.solve(), RMSE, plotting
10	Transforming samples and polynomial regression	Vandermonde-matrix, np.power.outer()
10	Fitting and evaluating linear models with regularization	Numerical stability, condition number, plotting
15	Fitting a linear model on real datasets	scikit-learn: StandardScaler(), LinearRegression()

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

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities