Statistical Signal Processing

Lecture: Probability and Random Variables and Classical Estimation Theory
[part 1] [part 2] [part 3]
- Dr James Hopgood

Lecture Handout: Probability and Random Variables and Classical Estimation Theory
- Dr James Hopgood

Lecture: Introduction to Random Processes
- Dr James Hopgood

Lecture Handout: Introduction to Random Processes
- Dr James Hopgood

Lecture: Optimal and Adaptive Filtering
- Dr Murat Uney

Lecture Handout Optimal and Adaptive Filtering
- Dr Murat Uney

Tracking

Web-Link to Notes: Tracking and Finite Set Statistics
- Dr Daniel Clark

Lecture Handout: Introduction to Particle Filtering
- Jose Franco

Lecture: Recent Advances in Multi-Object Estimation
- Dr Emmanuel Delande

Lecture: Multi-Object Modelling of Clutter Processes
- Dr Isabel Schlangen

Basic Concepts For Multi-Object Estimation
- Daniel Clark, Emmanuel Delande, Jeremy Houssineau

Pattern Recognition and Classification

Lecture: Pattern Recognition
- Prof Josef Kittler

Lecture: Dimensionality Reduction
- Prof Josef Kittler

Lecture: Deep Learning and its application to CV and NLP
- Dr Fei Yan

Lecture: Support Vector Machines
-  Prof Sangarapillai Lambotharan

Source Separation

Lecture: Introduction to Source Separation
- Dr Wenwu Wang

Lecture: Principle Component Analysis
- Dr Mohsen Vaqvi

Lecture: Convolutive Source Separation
- Dr Wenwu Wang

Lecture: Polynomial Matrices & Decomposition; Applications to Beamforming and Source Separations
- Dr Stephan Weiss

Polynomial Matrices - Selected Papers