Probabilistic and Bayesian Modelling I

author: Manfred Opper, Department of Artificial Intelligence, TU Berlin
published: Feb. 25, 2007,   recorded: April 2006,   views: 17459


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Part 4 24:13


There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw data by identifying the useful patterns and structures that underlie it. This module introduces adaptive and probabilistic approaches to modeling such complex data. We first consider finding structure in high-dimensional data. The kernel methods approach to identifying non-linear patterns in introduced while addressing the issues of statistical reliability of inferences made from limited data. Subspace identification is considered and correlations across different data modalities are shown to provide a useful approach to eliciting semantic representations. The final section of the course will introduce learning probabilistic models, (e.g. in biological sequence data), fusing prior knowledge and data, complex and approximate inference.

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Reviews and comments:

Comment1 Hayri Volkan Agun, July 12, 2008 at 11:13 a.m.:

Some of these videos of this lecture are not complete. Last 2 minutes of some of them is missing(It might be third one).
This series from Manfred Opper is quite useful to understand the nature of probability and some basic concepts from Mathematical view point. Not much of the lecturers talk about mathematical proofs so listening Manfred's lectures are like finding a water source in a desert.
His tone of voice and stability is quite professional. The lectures includes the topics of probabilistic models like Gaussian, EM and more of their mathematics.

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