Probabilistic Graphical Models
(Formerly Machine Learning: Statistical Approaches II)
Winter Semester 2014/2015
The course covers advanced topics in machine learning, for example: Graphical models in machine learning, inference mechanisms and sampling strategies in graphical models, Gaussian processes, probabilistic topic models, unsupervised and semisupervised learning.
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Organization
Module no.  20000449iv 
Event type  integrated course (IV4, 6CP, 4SWS), elective 
Being taught  regularly, usually winter semesters 
Time  Wednesdays, 09:50 – 13:20 
First class  15.10.2014 
Location  S305, room 073 
Lecturer  Prof. Stefan Roth, Ph.D. 
Exam  Usually individual oral exams. 
Content
 Refresher of probability & Bayesian decision theory
 Directed and undirected models and their properties
 Inference in tree graphs
 Approximate inference in general graphs: Message passing and mean field
 Learning of directed and undirected models
 Sampling methods for learning and inference
 Modeling in example applications, including topic models
 Deep networks
 Semisupervised learning
Learning goals
After successfully attending the course, students have developed an indepth understanding of probabilistic graphical models. They describe and analyze properties of graphical models, and formulate suitable models for concrete estimation and learning tasks. They understand inference algorithms, judge their suitability and apply them to graphical models in relevant applications. Moreover, they determine which learning algorithms are suitable to estimate the model parameters from example data, and apply these.
Degree courses
Can be taken for credit toward BSc / MSc Informatik, MSc Visual Computing, MSc Autonome Systeme, BSc / MSc Computational Engineering and others. Students from other departments, e.g. Mathematik, Elektrotechnik, IST, or Physik are welcome, though academic credit may need to be arranged.
Prerequisites
It is recommended to have taken Statistical Machine Learning (formerly Machine Learning: Statistical Approaches I).
Literature
