Topic outline

  • Machine Learning

  • Introduction

  • Gaussian Graphical Models

  • Gaussian Processes and Bayesian Optimization

  • Kernel methods, Splines

  • Logistic Regression, LDA, QDA, Examples

  • Model Assessment

  • Decision Trees, MARS

  • Ensamble Learning

  • Clustering

  • Bayesian Methods, EM algorithm

  • Association Rule Learning

  • Inductive Logic Programming

  • PCA Extensions, Independent Component Analysis

  • Support Vector Machines