Welcome to Machine learning with probabilistic graphical models

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
Format: Two full days with lectures and one hand-in assignment
Prerequisites: Basic knowledge in probability theory
Learning objectives: Obtain knowledge and application skills about the use of graphical models and methods for probabilistic machine learning 

Organizer: Manfred Jaeger and Thomas Dyhre Nielsen

Lecturers: associate professor Manfred Jaeger, jaeger@cs.aau.dk, and associate professor Thomas Dyhre Nielsen, tdn@cs.aau.dk


Time: May 14-15, 2019  

Place: Selma Lagerløfs vej 300,  Aalborg

Zip code: 

Aalborg Øst

Number of seats: 30

Deadline: April 23rd, 2019

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 5,000 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 three 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.