Kernel Learning: A Comprehensive Introduction

Welcome to the Kernel Learning repository! This project provides a comprehensive introduction to kernel methods in machine learning. It covers the theoretical foundations as well as practical aspects such as Support Vector Machines (SVMs) and scalable techniques like Random Fourier Features.

Overview

This repository contains:

• A detailed report written in LaTeX that explains the core concepts behind kernel learning, including:
  • Supervised Learning and Empirical Risk Minimization (ERM)
  • Positive Definite Kernels and the Kernel Trick
  • Reproducing Kernel Hilbert Spaces (RKHS) and the Representer Theorem
  • Support Vector Machines (SVMs)
  • Large-Scale Kernel Learning and Random Fourier Features
• The corresponding presentation hold at TUM (Technical University Munich)
• Example code and experiments that illustrate these concepts.