Administrative information


Title Decision trees
Duration 2 x 45 mins
Module A
Lesson Type Practical
Focus Practical - AI Modelling
Topic Data analysis

 

Keywords


Dataset generation,Fitting,Model complexity,

 

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

None.

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.

You can base this class around the notebooks.

Outline/time schedule


 
Duration (min) Description Concepts Activity Material
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

 

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