In this lecture, we will look at support vector machines. We will first look at the connection between maximizing margins and learning linear classifiers. This will give us an objective function for training, namely the SVM objective. This objective is our first sight of the idea of regularized risk minimization.
There are several algorithms for optimizing the SVM objective. We will look at a simple, yet effective, one: stochastic subgradient descent and explore the connection with the perceptron algorithm.
Lectures
 Videos:
 Older videos:
Readings

Chapters 3 and 6 of Hal DaumÃ© III, A Course in Machine Learning

Chapters 14, 15, 16 of Shai ShalevShwartz and Shai BenDavid, Understanding Machine Learning: From Theory to Algorithms (Available online)
Additional reading

A talk on Optimization, Support Vector Machines, and Machine Learning that goes into the details of primal and dual forms of SVMs and optimization.