Course info

Description:
This course gives an introduction to probabilistic graphical models, which play an important role in modern machine learning and artificial intelligence. Various types of graphical models are introduced, and their use in typical application domains illustrated (e.g., Hidden Markov Models for biological sequence analysis, Markov Random Fields for Image Processing). The course emphasizes both modeling and learning aspects of graphical models. With regard to modeling, we discuss underlying probabilistic (independence) assumptions that need to be made when choosing a particular type of model for a given application domain. Also limitations imposed by the computational complexity of inference tasks for a given model are considered. With regard to learning, the most important paradigms for learning graphical models from data are explained, and exemplified by typical learning tasks.

Outline of contents:

Basics of probability
- Joint probability distributions
- Conditional probability and independence

Directed and undirected graphical models
- Bayesian and Markov networks, factor graphs
- Graph structure and independence relations: d-separation and the Hammersley-Clifford theorem

Latent variable models
- Hidden Markov models
- Unsupervised learning
- Template models
- Plate representation

Learning and inference
- Structure learning
- Parameter estimation
- Supervised learning
- Bayesian learning

Organizer: Thomas D. Nielsen

Lecturers: Thomas D. Nielsen, Manfred

ECTS: 2.0

Time: 17 and 19 May 2022

Place:  SLV 300 i lokale 0.2.13 fra kl. 9.00-16.00. In-person attendance. 

Number of seats: 25

Deadline: 10 April 2022

Important information concerning PhD courses:
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.