Computational Intelligence Lectures
Course Scripts and Lecture Material
Part I (Anand, Guillaume)
- 06.03.2018
- Lecture 1
- 20.03.2018
- Lecture 2
- 10.04.2018
- Lecture 3
- 17.04.2018
- Lecture 4
- 24.04.2018
- Lecture 5
- 02.05.2018
- Lecture 6
Further reading material can be found under Bibliography.
2016 materials:
Part II (Pernkopf)
Script and course notes part 1, part2.
In addition, the slides of the HMMs and the tutorial + slides of the Graphical Models are important. From the tutorial Section 3, 4.1, 4.2, 5.1, 5.2, 5.4, and 7 (without sub-chapters) are relevant.
Links to relevant book chapters
Course overview: Parametric & Non-Parametric Density Estimation
- Chapter 3.1 - 3.5 & 4.1 - 4.3 in R. O. Duda, P.E. Hart, D. G. Stork: Pattern Classification, 2nd edition, John Wiley & Sons, 2001.
Bayes Classifier:
- Chapter 2.1 - 2.6 in R. O. Duda, P.E. Hart, D. G. Stork: Pattern Classification, 2nd edition, John Wiley & Sons, 2001
- Tutorial
Gaussian Mixture Model & k-means:
- Chapter 9 in C.Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2006.
Markov Model & Hidden Markov Model:
- Chapter 13 in C.Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2006.
- Slides
- Tutorial
Graphical Models:
- Chapter 8 in C.Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2006 (available for free).
- Lecture Slides
- F. Pernkopf, R. Peharz, and S. Tschiatschek, Introduction to Probabilistic Graphical Models, parts of Section 3, 4.1, 4.2, 5.1 (Bayesian networks), 5.2 (Markov networks), 5.4 (Markov chain), 7 (inference), 7.1 (exact inference), and 8.1 (HMMs, evaluation problem) are relevant.
PCA & LDA:
- Chapters 12.1, 4.1.4, 4.1.6 in C.Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2006.
- Chapter 3.8 in R. O. Duda, P.E. Hart, D. G. Stork: Pattern Classification, 2nd edition, John Wiley & Sons, 2001.
- Tutorial