Lectures

Introductory part

  • Lecture 1: Introduction & Motivation
    Tue 24.10 12:15, Liivi 2-315
    • Slides: (pdf)
    • Auxiliary material:
      • A Brief Introduction to Matrix Algebra: (pdf)
  • Lecture 2: Basics of Optimization
    Tue 31.10 12:15, Liivi 2-315
  • Lecture 3: Basics of Probability & Statistics
    Tue 07.11 12:15, Liivi 2-315

Main part

The contents of the lectures is based on joint work of Tijl De Bie, Nello Cristianini, and John Shawe-Taylor

  • Lecture 1 (A and B): A bird's eye perspective
  • Lecture 2 (A and B): Challenges in automatic pattern analysis
  • Lecture 3 (A and B): Patterns in numbers and vectors
  • Lecture 4 (A and B): Getting a grip on high-dimensional data
  • Lecture 5 (A and B): Supervised learning
  • Lecture 6 (A and B): Advanced topics
  • Some relevant references
    • Slides (still subject to some additions): (pdf)
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