Probabilistic Machine Learning (PML) PhD course (5+3hp)
Spring 2018
Description
Data is becoming more and more widely available and the world is now in a situation where there is more data than we can handle. This clearly calls for new technology and this challenge has resulted in the rapid growth of the machine learning area over the past decade. This course provides an introduction into the area of machine learning, focusing on dynamical systems. To a large extent this involves probabilistic modelling in order to be able to solve a wide range of problems.
Contents
- Linear regression
- Linear classification
- Support vector machines
- Gaussian processes
- Expectation Maximization (EM)
- Neural networks
- Clustering
- Variational inference
- Graphical models and probabilistic programming
- Message passing algorithms
Course Structure
The course gives 5 hp (you can receive an additional 3 hp by carrying out a project).
- Lectures: 11
- Problem solving sessions: Coordinated by Carl Andersson
and Anna Wigren. - Project: Optional
Examination
The examination consists in a standard written 2 day (48 h) exam. The exam period is June 5, 2018 until July 6, 2018. See slides 4-5 of Lecture 7
for more information about the exam.
Course literature
The main book used during the course is,
[B] Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
We will also make use of,
[HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction, Second edition, Springer, 2009.
Recommended supplementary reading
There are by now many books written on the machine learning subject and new books keeps appearing all the time. Here are links to a few additional resources.
- Kevin P. Murphy. Machine learning - a probabilistic perspective, MIT Press, 2012.
- Daphne Koller and Nir Friedman. Probabilistic Graphical Models Principles and Techniques, MIT Press, 2009.
- David Barber. | Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.
- Sergios Theodoridis. Machine Learning, A Bayesian and Optimization Perspective, 2015.
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning - with Applications in R, Springer, 2013. (provides a nice introduction to the area of statistical machine learning for non-mathematical sciences)
Periodicity
We are no longer offering this course. Instead we refer to the courses Statistical Machine Learning and Advanced Probabilistic Machine Learning.
Previous editions have been given at Uppsala University (2018, 2016, 2014), Linköping University (2013, 2011) and at Lund University (2011).
Schedule
Course level
This is a PhD level course.
Prerequisites
Basic undergraduate courses in linear algebra, statistics and optimization.
Related Courses
Statistical estimation theory and its applications.
Contact Person
Thomas Schön, email: thomas.schon_at_it.uu.se.