Data mining: kernel methods and neural networks · School of Engineering and Data Science

Introduction

What this guide covers

Introduction Advanced data mining module

Overview

Topics covered

• Introduction
• Kernel Methods and Neural Networks: From high dimension to deep learning
• The problem of nonlinearity in data mining
• High-dimensional feature spaces (Feature Spaces)
• The Kernel Trick and Mercer's Conditions
• Support Vector Machines (SVM): Linear and non-linear classification

Related concepts

Main connections

• Neural networks & deep learning
• Linear algebra & matrices
• Regularization (Lasso / Ridge / Elastic Net)
• Calculus & differentiation
• Supervised machine learning
• Principal component analysis (PCA)

Compatible notation

Mathematical sample in LaTeX

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