Administrative information

Title	Lecture: SVMs and Kernels
Duration	60
Module	A
Lesson Type	Lecture
Focus	Practical - AI Modelling
Topic	AI Modelling

Keywords

maximum margin classifier, support vector, kernel trick,

Learning Goals

To know what a support vector is and how to find it in a feature space
To know how a linear SVM works
To understand the concept of kernel function in the context of SVM
To know how the kernel trick allows to perform non linear classification with linear SVM

Expected Preparation

Learning Events to be Completed Before

Lecture: Model Fitting and Optimization

Obligatory for Students

Review of analytic geometry (e.g. distance of a point to a plane).

Optional for Students

None.

References and background for students:

Bishop, Christopher M. (2006). Pattern recognition and machine learning, Chapter 7

Recommended for Teachers

Familiarize themselves with the demonstration material.

Lesson Materials

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

Cover the topics in the lesson outline and demonstrate the effect of the complexity parameter and RBF parameter using the interactive notebooks. Show an example of underfitting. Give a brief overview of the code.

Outline/time schedule


Duration (min)	Description	Concepts
15	Maximum margin classifiers	feature space, separating hyperplane, margin, support vector
10	Soft-margin formulation	slack variables, model complexity
10	Dual formulation and optimization	Lagrange multipliers, primal and dual problems
10	Support vectors and predictions	dual parameters and support vectors
15	Non-linearization and the kernel trick	kernel function

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