Contents

Content Description:

The lecture teaches basic methods in machine learning, including but not limited to:

• Supervised Learning (classification)
  • Naive Bayes
  • Classification Trees
  • Combination Methods
  • Support Vector Machine
  • Neural Networks
  • Genetic Algorithms
• Unsupervised Learning (Cluster analysis)
  • K-Means
  • SOM
  • Isomap
  • Model based Clustering

We will demonstrate some of the concepts through practical exercises in Python and/or R.