The course covers some advanced models in machine learning.
The models are analyzed mainly from a Bayesian perspective.
The course is also a master's course on the LiU programme 'Statistics and Machine Learning'.
The course is organized into four topics:
- Graphical Models
- Hidden Markov Models
- Gaussian Process Regression and Classification
- State-space models
Each topic includes lectures, a computer lab and a follow-up seminar.
Submitted by Lars Forsberg on September 8, 2016 - 10:55
The course offers an introduction to financial econometrics for second-cycle studies. It
covers the main parts of the spectrum of quantitative financial economics, discusses
important results in the empirical finance literature, and provides a comprehensive
knowledge to do empirical work in financial practice.
A student who has taken the course should:
- have a solid knowledge about basic themes in financial econometrics;
- know and be able to use concepts and notation that is frequently used in financial
econometrics;
This masters course introduces basic concepts, ideas and methods of Bayesian inference. It is given in the late fall at Stockholm University as part of the masters programme and in the late spring at Linköping University (then under the name Bayesian Learning) as part of the master's course in the programme 'Statistics and Machine Learning'. The focus is on models and methods in the field of machine learning, but most of the course content is of general statistical interest.
The course will be in English.
Submitted by MariaKarlsson on September 18, 2012 - 13:03
In this course we introduce causal inference with main focus on two approaches; i) the potential outcome framework/ Rubin Causal Model and ii) Causal inference with graphical models.
Identification of causal effects in the two approaches is studied. Non and semi-parametric estimators of causal effects commonly associated with the potential outcome framework will be presented. A general introduction to graphical models is also given.
Starting November 5, I will give a course in asymptotic theory at the PhD level in Statistics. The course book is Ferguson, T.S. A
Course in Large Sample Theory, Chapman and Hall 1996. Lectures are at 10.15-14 on Mondays, ending December 17. Examination is through take home exercises. For further information, see the attached schedule. Welcome to the course!
