Welcome to the Machine Learning Course for Black and Indigenous Students!

This program is offered by Vector Institute in its drive to build research and expand career pathways in the field of AI for under-represented populations.

Instructor: Bonaventure Molokwu | Tutorial Developer: Manmeet Kaur Baxi | Course Tutors: Yinka Oladimeji and Manmeet Kaur Baxi | Course Director: Shingai Manjengwa (@Tjido)

Never stop learning!

Support Vector Machines (SVM)

Generally, considered to be a classification approach, it can be employed in both types of classification and regression problems. It can easily handle multiple continuous and categorical variables.
Known for its kernel trick to handle nonlinear input spaces. (It enables us to implicitly map the inputs into high dimensional feature spaces.)
Offers very high accuracy compared to other classifiers such as logistic regression, and decision trees.
Applications: face detection, intrusion detection, classification of emails, news articles and web pages, classification of genes, and handwriting recognition.

Intuition

SVM constructs a hyperplane in multidimensional space to separate different classes.
SVM generates optimal hyperplane in an iterative manner, which is used to minimize an error.
The core idea of SVM is to find a maximum marginal hyperplane(MMH) that best divides the dataset into classes.

Terminology

Support Vectors: The data points, which are closest to the hyperplane. These points will define the separating line better by calculating margins. These points are more relevant to the construction of the classifier.
Hyperplane: A decision plane that separates between a set of objects having different class memberships.
Margin: A gap between the two lines on the closest class points. This is calculated as the perpendicular distance from the line to support vectors or closest points. If the margin is larger in between the classes, then it is considered a good margin, a smaller margin is a bad margin.

How does it work?

The main objective is to segregate the given dataset in the best possible way. The distance between the either nearest points is known as the margin. The objective is to select a hyperplane with the maximum possible margin between support vectors in the given dataset.

SVM searches for the maximum marginal hyperplane in the following steps: