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Introduction to Machine Learning (2VL)

Motivation:

Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods have become indispensable in various fields, such as, process modeling, computer vision, signal processing, speech and language processing, life sciences, and so forth. This course gives a beginners' introduction to machine learning. It features the most essential concepts as well as it gives an overview of the most important methods. The methodological subjects are complemented by examples of exciting recent real-world applications of machine learning methods.

Topics:

  • Taxonomy of machine learning: supervised vs. unsupervised learning, reinforcement learning, classification vs. regression
  • Examples of basic methods: nearest neighbor, linear regression, k-means, principal component analysis
  • Basics of evaluating machine learning models: confusion tables, ROC curves
  • Support vector machines and random forests (+ examples from life sciences)
  • Neural networks and Deep Learning (+ examples from image analysis, drug design, and language processing)
  • Clustering and biclustering

 

Organizational details

  • Electronic course material is made available for download