Syllabus/achievement requirements

Syllabus

1. The lecture notes (you can find them in the Schedule)
2. A.E. Eiben and J.E. Smith: Introduction to Evolutionary Computing, 2nd printing, 2007. Springer. ISBN: 978-3-540-40184-1
   • ??Chapter 1 - Introduction (lecture 2)
   • Chapter 2 - What is an Evolutionary Algorithm? (lecture 2)
   • Chapter 3 - Genetic Algorithms (lecture 3)
   • Chapter 4 - Evolution Strategies (lecture 2)
   • Chapter 5 - Evolutionary Programming, sections 1, 3-8 (lecture 2)
   • Chapter 6 - Genetic Programming (lecture 2)
   • Chapter 9 - Multi-Modal Problems and Spatial Distribution (lecture 4)
   • Chapter 10 - Hybridisation with Other Techniques: Memetic algorithms (lecture 4)
   • Chapter 14 - Working with Evolutionary Algorithms (lecture 4)
3.  S. Marshland: Machine learning: An Algorithmic Perspective. ISBN:978-1-4200-6718-7
   • ??Chapter 1 - Introduction (lecture 6)
   • Chapter 2 - Linear Discriminants (lecture 6)
   • Chapter 3 - The Multi-Layer Perceptron (lecture 7)
   • Chapter 5 - Support Vector Machines (lecture 8)
   • Chapter 7 - Decision by Committee: Ensemble Learning (lecture 8)
   • Chapter 9 - Unsupervised Learning (lecture 9)
   • Chapter 10 - Dimensionality Reduction, section 2 (lecture 8)
   • Chapter 11 - Optimisation and Search, sections 1, 4-6 (lecture 1)
   • Chapter 13 - Reinforcement Learning (lecture 10)
4. On-line papers (Both are available for download when on the UiO network)
   • Particle Swarm Optimization (only the section about PSO in the pdf) (lecture 5)
   • Cartesian Genetic Programming chapter (you can skip section 2.5) (lecture 5)

Obligatory Mid-Term Exercises (each exercise is PASS/FAIL):

1. Two exercises on evolutionary algorithm and machine learning.
2. Students registered for INF4490 will be given additional excercises within area of the course.