Administrative information

Title	Lab session: SVMs and Kernels
Duration	2x45
Module	A
Lesson Type	Practical
Focus	Practical - AI Modelling
Topic	AI Modelling

Keywords

support vector machine,kernel function,RBF,model complexity,

Learning Goals

To be able to design and use a linear SVM
To be able to design ad use a non-linear SVM, using a kernel
To design and optmize a SVM (cookbook approach)

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Review of basic Python.
Review of using scikit-learn models.

Optional for Students

None.

References and background for students:

Hands on Machine Learning with Scikit-Learn and Tensorflow by Aurélién Géron
Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido
Udemy course – Machine Learning – A Z by Kirill Eremenko and Hadelin de Ponteves
Udemy course – Feature Engineering for Machine Learning by Soledad Galli
Support-vector machine on wikipedia
https://www.datacamp.com/community/tutorials/svm-classification-scikit-learn-python
http://dataaspirant.com/2017/01/13/support-vector-machine-algorithm/
https://www.ritchieng.com/machine-learning-evaluate-classification-model/
https://en.wikipedia.org/wiki/Kernel_method
https://en.wikipedia.org/wiki/Polynomial_kernel
https://en.wikipedia.org/wiki/Radial_basis_function_kernel
https://data-flair.training/blogs/svm-kernel-functions/

Recommended for Teachers

A review of how the pandas and scikit-learn functions used in the notebook are parameterized.

Lesson Materials

Notebook for SVMs and Kernels

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, sns and scikit-learn installed.

Outline/time schedule


Duration (min)	Description	Concepts
5	Brief of the tasks to be conducted
25	Data exploration and preprocessing	data description, missing values, feature distributions, outlier detection
30	Fitting SVM models	data scaling, linear SVM, RBF, model complexity
30	Model evaluation	hyperparameter optimization, underfitting, overfitting, ROC curve

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