Supervised, unsupervised and semi-supervised learning, Bayesian learning, Energy minimization and optimization.

QIS/LSF

Lecture: Monday 10:00-12:00, SR9

Tutorial & problem session: Monday 12:00-14:00, SR9

Written exam: 06.02.2017, 12:00-14:00, Magnus-Hörsaal

2nd written exam: 20.03.2017, 12:00-14:00, Robert-Mayer-Str. 11-15, SR 307

Results first exam: grades (Pose-Exam Review: 27.02.2017, 10:00 – 12:00, Raum 1.403, FIAS )

Schedule:

• 17.10.2016: Kaschube
• 24.10.2016: Bertschinger
• 31.10.2016: Ramesh
• 07.11.2016: Kaschube (Methods)
• 14.11.2016: Kaschube (Methods/applications)
• …
• Last part of course: Ramesh (Applications/methods)

Material

Slides 17.10.2016

Slides_24.10.2016

Slides Tutorium 24.10

Exercise_1

Slides Tutorium 31.10.

Slides 07.11.2016

Exercise_2

Slides Tutorial Week 5 Normal Distribution

Exercise_3

Slides week 5

Exercise_4

Slides week 6

Exercise_5

Slides week 7

Exercise_6

Slides week 8 (updated)

Exercise_7

Slides week 9

Exercise_8 , data.zip

Slides week 10

Exercise_9

Slides week 11

exercise_10 , wine ( dataset description)

Slides Week 12, 13, 14 – 09_Classification 06_Learning_And_Inference

Slides Week 12 – ML link to Datascience

exercise_11, FreqBayes.ipynb

Slides Week 14 – Randomized forests (Shotton, ICCV 2009 tutorial) ICCV2009TutorialPartI