notes

A collection of my lecture notes.

supervised learning

Chapter 11: Advanced Risk Minimization

This chapter revisits the theory of risk minimization, providing more in-depth analysis on established losses and the connection between empirical risk minimization and maximum likelihood estimation.

Chapter 12: Multiclass Classification

This chapter treats the multiclass case of classification. Tasks with more than two classes preclude the application of some techniques studied in the binary scenario and require an adaptation of loss functions.

Chapter 13: Information Theory

This chapter covers basic information-theoretic concepts and discusses their relation to machine learning.

Chapter 15: Regularisation

Regularization is a vital tool in machine learning to prevent overfitting and foster generalization ability. This chapter introduces the concept of regularization and discusses common regularization techniques in more depth.

Chapter 16: Linear Support Vector Machine

This chapter introduces the linear support vector machine (SVM), a linear classifier that finds decision boundaries by maximizing margins to the closest data points, possibly allowing for violations to a certain extent.

Chapter 17: Nonlinear Support Vector Machines

Many classification problems warrant nonlinear decision boundaries. This chapter introduces nonlinear support vector machines as a crucial extension to the linear variant.