Title | Decision trees | ![]() |
Duration | 2 x 45 mins | |
Module | A | |
Lesson Type | Practical | |
Focus | Practical - AI Modelling | |
Topic | Data analysis |
Dataset generation,Fitting,Model complexity,
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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.
You can base this class around the notebooks.
Duration (min) | Description | Concepts | Activity | Material |
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5 | Brief of the tasks to be conducted | Introduction | Lecture | |
5 | Generating a two-dimensional dataset | Gaussian noise, np.random.randn() | Coding | Jupyter notebook |
10 | Fitting and evaluating a Decision Tree | scikit-learn | Coding | |
15 | Investigating the effect of the model complexity parameter | model complexity, support vectors, margin, plotting | Documentation | |
5 | Fitting the Model | Model Fitting | Coding | |
15 | Evaluate and Investigatie the effect of the parameter | Parameters Evaluation | Documentation | |
20 | Implementing a custom precomputed Model | matrix operations in numpy | Coding | |
45 | Fitting and evaluating the model on real data | data preprocessing (scaling), Accuracy, Confusion Matrix, Cross Validation | Documentation |
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 |