Introduction

Content

• Machine Learning Problems

  • Classification, Regression, Annotation
  • Forecasting
  • Novelty detection

• Data

  • Labeled, unlabeled
  • Semi-supervised, transductive, responsive environment, covariate shift

• Applications

  • Optical character recognition
  • Bioinformatics
  • Computational advertising
  • Self-driving cars
  • Network security

• Basic tools

  • Linear classification, regression
  • Feature maps
  • Trees
  • Instance based classifiers

• Challenges

  • Model selection, underfitting, overfitting
  • Validation, confidence
  • Explore * exploit * reactive environment

Supplementary material

Slides in PDF and Keynote. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it. ## Videos This is unedited video straight from a Lumix GF2 with a 14mm lens which should explain the sound (it doesn’t have a dedicated audio input) … But it should help as a supplement with the slides.