Supervised, unsupervised and semi-supervised learning, Bayesian learning, Energy minimization and optimization.
The first lectures and exercises will be based on the book
Pattern recognition and machine learning by Christopher Bishop.
Your lecturers are Prof. Dr. Nils Bertschinger, Prof. Dr. Matthias Kaschube and Prof. Dr. Visvanathan Ramesh.
Most of the examples will be in python, with toolkits like scikit-learn (sklearn), numpy and scipy.
( if you are more proficient in R, Matlab, C++ or other languages and know which packets to use for the exercises, you might also use these languages for submissions, but without or only limited support from our side )
Available material and exercise sheets will be posted here.
Summary Prof. Bertschinger’s part: Covered
Exercise Sheet 1 ( 16.10.2015 ): Exercises 1 – 16.10.2016
Exercise Sheet 2 ( 23.10.2015 ): Exercise 2 – 23.10.2015
Exercise Sheet 3 ( 30.10.2015 ): Exercise3, testXY, trainXY – solution3.py
Exercise Sheet 4 ( 09.11.2015 ): Exercise4 – solution4
Exercise Sheet 5 ( 13.11.2015 ): Exercise5 , comp_trainX , comp_trainY
Slides week 6 and 7: Slides ML1516 Week6-7
Slides week 8: week8
Exercise Sheet 6 ( 04.12.2015 ): Exercise_week_6_and_7
Exercise Sheet 7 (11.12.2015): Exercise_week_9
Slides (11.12.2015): 05_Normal_Distribution
Slides (15.12.2015): weeks9_10
Exercise Sheet 8 (week 10): Exercise_week_10
Exercise week 11: exercise_11 ,pca_faces, hmm.zip
Slides: 06_Learning_And_Inference , 07_Modeling_Complex_Densities , PRG1EPR_LecJan152016
Exercise week 12: exercise_12
Slides: EMLecture_VR_2016
Slides (29.01.2016): 07_Modeling_Complex_Densities , 13_Preprocessing , EMLecture_VR_2016
Slides 05.02.2016: http://www.cse.psu.edu/~rtc12/CSE486/lecture24.pdf
Exercise week 13: exercise_13