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


 

Expected Preparation


Learning Events to be Completed Before

Obligatory for Students

Optional for Students

None.

References and background for students:

Recommended for Teachers

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

HCAIM Consortium