Each talk will be a 90 minute tutorial, followed by about 30 minutes of question/discussion time.
All lectures will take place on Thursdays, from 4pm to 6pm, in Lecture Room 4, in the Engineering Department, Trumpington Street, Cambridge [map].
Lectures are open to any one interested.
2006 Lecture series here.
|Thurs Feb 1, 4pm||Dr Paul M Newman||
Robot Localisation and Mapping
This tutorial will cover the area of mobile robot localisation and mapping. Beginning with the simple cases of Bayesian localisation and feature based mapping, the tutorial will progress to consider the full Simultaneous Localisation and Mapping (SLAM) problem. This is in many ways a cornerstone of current and future autonomous mobile systems and has been a subject of intense research over the last five years. If we want to send machines into a-priori unknown settings we need robust, embedded SLAM - something that still eludes us. Both Kalman and sample based approaches will be covered. The tutorial will also give time to some major outstanding issues in the area--in particular the loop closing problem (the task of deciding if the current local workspace intesects with an already mapped region) and recent work on appearance based methods.
| Thurs Feb 22, 4pm
(Note: Lecture in LR5)
|Prof Zoubin Ghahramani||
An Introduction to Non-parametric Bayesian Methods
Bayesian methods provide a sound statistical framework for modelling and decision making. However, most simple parametric models are not realistic for modelling real-world data. Non-parametric models are much more flexible and therefore are much more likely to capture our beliefs about the data. They also often result in better predictive performance.
I will give a survey/tutorial of the field of non-parametric Bayesian statistics from the perspective of machine learning (a slightly revised version of my tutorial at the 2005 UAI Conference). Topics will include:
|Thurs Mar 1, 4pm||Dr Yee Whye Teh||
Dirichlet Processes and Hierarchical Dirichlet Processes
Dirichlet processes (DPs) are the most widely used class of Bayesian nonparametric models. DPs are most commonly used for mixture modelling where the nonparametric nature of DPs provide an elegant alternative to model selection of finite mixtures. Hierarchical Dirichlet processes (HDPs) are an extension of DPs to mixture modelling of grouped data, where mixture components can be shared across different groups. I shall give an in depth tutorial into both DPs and HDPs. In particular I shall cover the different representations of DPs and HDPs and applications of DPs and HDPs in a variety of fields. If time permits I shall touch upon generalizations of these models and inference schemes.
|Thurs Mar 15, 4pm||Dr Ralf Herbrich||
In this talk I will present a Bayesian approach to ranking a set of objects based on the possibly partial or noisy rankings of small subsets of objects. Rankings are represented by assigning a latent real-valued variable (skill, urgency, value) to each object and sorting the objects according to the magnitude of the latent variables. The system maintains a Gaussian belief about the value of each object in terms of mean and variance. I will discuss approximate message passing in factor graphs as the computational technique to address the problem of inference.
After presenting theoretical and algorithmic aspects of the system, I will outline two applications: