8 years 7 years

Lecture notes

Assignments

  1. Write a linear support vector machine (10%)
  2. Use of Hidden Markov model (10%)
  3. Write feature selection methods and try them (10%) (no objectives yet)
  4. Use of Evolutionary computing methods (10%) (no objectives yet)

Not published for avoiding plagiarism

Topics

  • Symbolic Learning (generic term)
  • Statistical Learning (generic term)
  • Artificial Neural Networks (partly touched lecture 3)
  • Support Vector Machines (lecture 1)
  • Cluster Analysis (not covered)
  • Fuzzy Logic (partly touched lecture 3)
  • Evolutionary Computation (lecture 6)
  • Hybrid Intelligent Methods (lecture 3)

Talked about but not covered

  • Gaussian Mixture Models
  • Random forests
  • Bayesian Neural Networks
  • Convex optimizations

Books

  • Machine Learning and Data Mining: Introduction to Principles and Algorithms (same as ML1)
  • Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and David G. Stork

Evaluation

  • 3 hour written exam (60%)
  • 4 individual homework deliveries (4*10%)