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Syllabus

1. The lecture notes.
2. A.E. Eiben and J.E. Smith: Introduction to Evolutionary Computing, Second Edition (ISBN 978-3-662-44873-1, ). 

   • Chapter 3-4 (except 3.6 and 4.4.2)

   • Chapter 5 (except 5.2.6, 5.4, 5.5.7)

   • Chapter 10 (except 10.4, 10.5, 10.6)

   • Chapter 12 (except 12.3.4 and 12.4)

3.  S. Marsland: Machine learning: An Algorithmic Perspective. SECOND EDITION (2015), ISBN: 978-1-4665-8328-3,  (ethernet machine or by logging in through ) BUT only for TWO concurrent users

   • Chapter 1

   • Chapter 2, From beginning up to formula (2.2) p. 23 and section 2.5 (except the formulas)

   • Chapter 3 (except the proof of The Perceptron Convergence Theorem in sec. 3.4.1)

   • Chapter 4 (except 4.2.6 and the technical details of 4.6)

   • Chapter 6, section 6.2 (except 6.2.2)

   • Chapter 7, section 7.2 (except subsections 7.2.1-7.2.2)

   • Chapter 9, sections 9.1, 9.4-9.6 (except 9.1.1)​

   • Chapter 11

   • Chapter 13, sections Introduction, 13.2, and 13.3

   • Chapter 14, sections 14.1

4. Hal Daumé III,
   • Chapter 1, sections 1.0-1.3 (except the pseudo code)
   • Chapter 2, sections 2.2-2.7
   • Chapter 3, sections 3.0-3.3
   • Chapter 5, sections 5.0-5.6 (except precision-recall curves, ROC curves and AUC curves)

5. • Chapter 4, sections 4.7- 4.8

   • Chapter 5 (except 5.6.2 and the last paragraph of 5.5)

6. Wikipedia
   • , sections Introduction, 1, 3, 4, 7.1
   • , section 1

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

1. Passing three programming exercises (in Python) is required to take the exam.
2. Students registered for IN4050 will be given additional exercises within area of the course.