Title | Linear Regression | ![]() |
Duration | 60 | |
Module | A | |
Lesson Type | Practical | |
Focus | Practical - AI Modelling | |
Topic | AI modelling |
linear regression, maximum likelihood, maximum a posteriori, basis functions,
None.
The materials of this learning event are available under CC BY-NC-SA 4.0.
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.
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() |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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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 |