Instructor: |
Christoph Lampert |

Teaching Assistants: |
all of us |

Lectures: |
weekly on Tuesday, 10:15-11:30 and Thursday, 11:15- 12:30 |

Recitation: |
weekly on Tuesday, approx. 11:45-12:35 |

Location: | Central Building, Mondi 3 on Tuesday, Mondi 2 on Thursdays (new!) |

Website: |
official page (this one) |

announcements | schedule | references |

Formalities:

- Half semester course, 3 ECTS
- please register in iQ

- first meeting: Tuesday, November 29 (, because of a scheduling conflict)
- starting on Thursday, December 1, the course will take place at its regular time slots
- starting on Thursday, December 1, the course will be in Mondi 2 on Thursdays and Mondi 3 on Tuesdays
- the lecture and recitation on
**December 6 will take place**(originally, that wasn't clear because of travel) - there will be no lecture on December 8 because of the public holiday
- on January 10th, there will be a guest lecture by Sesh Kumar on approximate inference
- on January 24th, there will be a guest lecture by Vladimir Kolmogorov on energy minimization using graph cuts an related techniques
- Tuesday, January 24th, will be the last lecture. There will be final recitation afterwards, but no lecture on Thursday.

- exercise sheet 1: PDF (discussed at the recitation on Dec 6)
- Note: typos in 2d) and 2g) were fixed on 29/11/16 14:00 and 15:25
- exercise sheet 2: PDF (discussed at the recitation on Dec 13)
- exercise sheet 3: PDF (discussed at the recitation on Dec 20)
- exercise sheet 4: PDF (discussed at the recitation on Jan 10)
- exercise sheet 5: PDF (discussed at the recitation on Jan 17)
- exercise sheet 6: PDF (tentative, beware of typos; to be discussed at the recitation on Jan 24)

- presentation slides will be posted here after each lecture
- Lecture 1: Intro and Refresher of Probabilities PDF
- Lecture 2: Bayesian Networks (directed graphical models) PDF
- Lecture 3a: Excurse Causality PDF
- Lecture 3b: Markov Networks (undirected graphical models) PDF
- Lecture 4: Factor graphs, Parameter Estimation PDF
- Lecture 5: Maximum-A-Posteriori Estimation, Bayesian Inference, EM PDF
- Lecture 6: Inference and Learning in Hidden Markov Models PDF
- Lecture 7: Maximum Entropy Models, (Loopy) Belief Propagation PDF
- Lecture 8 (Sesh Kumar): Approximate Inference
- Lecture 9: Conditional Random Field Learning PDF
- Lecture 10: Energy Minimization, Structured Loss Functions PDF
- Lecture 11: Structured Support Vector Machines PDF
- Lecture 12: (Vladimir Kolmogorov): Graph cuts
- textbook: David Barber, "Bayesian Reasoning and Machine Learning", Cambridge University Press, 2011, ISBN-13: 978-0521518147
- more exhaustive textbook: Koller, Friedman,
*"Probabilistic Graphical Models: Principles and Techniques"*, The MIT Press, 2009, ISBN-13: 978-0262013192

announcements | schedule | references |