Melbourne School of Engineering
Nonlinear Signal Processing Laboratory

Annoucements

Friday the 26th of April.

Luenberger. Optimization by Vector Space Methods. (Prof. Jonathan Manton)

Time: 1pm to 2pm
Venue: Greenwood Theatre, Level 1, EEE Building

This book is a classic in its field.  It uses functional analysis to study infinite-dimensional optimisation problems.

 

Elliot et al. Hidden Markov Models: Estimation and Control. (Prof. Jonathan Manton)

Time: 2.30pm to 3.30pm
Venue: Greenwood Theatre, Level 1, EEE Building

This book will be taught in a way which makes it an introduction to stochastic filtering.  The advantage of Hidden Markov Models is that they are easy to understand yet quite powerful because they are nonlinear.  Optimal filters will be derived using the reference probability method.

 

 

Educational Activities – Fridays

An introduction to symbolic dynamics (Dr. Rika Hagihara)
Time & Venue: 11am to 12pm(Noon), Greenwood Theatre, Level 1, EEE Building.
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Synopsis: In symbolic dynamics Markov measures are described by a finite amount of data and can be realized naturally on subshifts of finite type. However, hidden Markov measures, the images under factor maps of Markov measures, do not always inherit properties of their pre-images.

In this lecture series we will first review the setting of symbolic dynamics: subshifts, shift transformation, and maps between subshifts. Our focus will be on subshifts of finite type and we will study their graph representations and topological entropy. We will then introduce Markov measures and consider when a given measure is hidden Markov. Along the way ideas from topological dynamics will be mentioned.

Working knowledge of point-set topology and linear algebra are prerequisites. Concepts and properties from measure theory will be explained as needed.

Please note that from this Friday, 26th of October, there will be 2 Symbolic Dynamic Courses. One at 11am and one at 1pm.

Educational Activities – Fridays

An introduction to symbolic dynamics (Dr. Rika Hagihara)
Time & Venue: 11am to 12pm(Noon), Greenwood Theatre, Level 1, EEE Building.
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Synopsis: In symbolic dynamics Markov measures are described by a finite amount of data and can be realized naturally on subshifts of finite type. However, hidden Markov measures, the images under factor maps of Markov measures, do not always inherit properties of their pre-images.

In this lecture series we will first review the setting of symbolic dynamics: subshifts, shift transformation, and maps between subshifts. Our focus will be on subshifts of finite type and we will study their graph representations and topological entropy. We will then introduce Markov measures and consider when a given measure is hidden Markov. Along the way ideas from topological dynamics will be mentioned.

Working knowledge of point-set topology and linear algebra are prerequisites. Concepts and properties from measure theory will be explained as needed.

 
 
NSP LAB EDUCATIONAL ACTIVITIES

Fridays

An introduction to symbolic dynamics (Dr. Rika Hagihara)
Time & Venue: 11am to 12pm(Noon), Greenwood Theatre, Level 1, EEE Building.

Synopsis: In symbolic dynamics Markov measures are described by a finite amount of data and can be realized naturally on subshifts of finite type. However, hidden Markov measures, the images under factor maps of Markov measures, do not always inherit properties of their pre-images.

In this lecture series we will first review the setting of symbolic dynamics: subshifts, shift transformation, and maps between subshifts. Our focus will be on subshifts of finite type and we will study their graph representations and topological entropy. We will then introduce Markov measures and consider when a given measure is hidden Markov. Along the way ideas from topological dynamics will be mentioned.

Working knowledge of point-set topology and linear algebra are prerequisites. Concepts and properties from measure theory will be explained as needed.

 

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Prof. Manton is working through the book titled “Differential Topology” by Victor Guillemin and Alan Pollack.
Target Audience: Students wishing to learn some rigorous mathematics relevant to engineering. 

 

Friday 3rd of August.
Please note that the Introduction to RKHS course has been completed.  

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Prof. Manton is working through the book titled “Differential Topology” by Victor Guillemin and Alan Pollack.

Target Audience: Students wishing to learn some rigorous mathematics relevant to engineering. 

 

Friday 27th of July.

Introduction to RKHS (Reproducing kernel Hilbert space) and their applications in signal processing (Dr. Pierre-Olivier Amblard)
Time & Venue: 11.15am to 12.15am, Greenwood Theatre, Level 1, EEE Building.
Topics to be covered:-
Intro to the math of RKHS
Simple applications in Machine learning (the representer theorem, regression, support vector machines)
Random variables with values in RKHS, application to dependence (or in-) measures
A Bayesian point of view

 

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Prof. Manton is working through the book titled “Differential Topology” by Victor Guillemin and Alan Pollack.

Target Audience: Students wishing to learn some rigorous mathematics relevant to engineering. 

 

Friday 20th of April.

Introduction to RKHS (Reproducing kernel Hilbert space) and their applications in signal processing (Dr. Pierre-Olivier Amblard)
Time & Venue: 11.15am to 12.15am, Greenwood Theatre, Level 1, EEE Building.

Topics to be covered:-
Intro to the math of RKHS
Simple applications in Machine learning (the representer theorem, regression, support vector machines)
Random variables with values in RKHS, application to dependence (or in-) measures
A Bayesian point of view

 

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

Prof. Manton is working through the book titled “Differential Topology” by Victor Guillemin and Alan Pollack.

Target Audience: Students wishing to learn some rigorous mathematics relevant to engineering. 

 

 

Friday 29th of June.

Introduction to RKHS (Reproducing kernel Hilbert space) and their applications in signal processing (Dr. Pierre-Olivier Amblard)
Time & Venue: 11.15am to 12.15am, Greenwood Theatre, Level 1, EEE Building. (Please note the time)

Topics to be covered:-
Intro to the math of RKHS
Simple applications in Machine learning (the representer theorem, regression, support vector machines)
Random variables with values in RKHS, application to dependence (or in-) measures
A Bayesian point of view

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.
Prof. Manton will teach from the book "Differential Topology".

 

Friday 15th of June.

Introduction to RKHS (Reproducing kernel Hilbert space) and their applications in signal processing (Dr. Pierre-Olivier Amblard)
Time & Venue: 11.15am to 12.15am, Greenwood Theatre, Level 1, EEE Building. (Please note the time)
Topics to be covered:-
**Intro to the math of RKHS
**Simple applications in Machine learning (the representer theorem, regression, support vector machines)
**Random variables with values in RKHS, application to dependence (or in-) measures
**A Bayesian point of view

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.

 

Friday 8th of June.
Introduction to RKHS (Reproducing kernel Hilbert space) and their applications in signal processing (Dr. Pierre-Olivier Amblard)
Time & Venue: 11.15am to 12.15am, Greenwood Theatre, Level 1, EEE Building. (Please note the time)
Topics to be covered:-
**Intro to the math of RKHS
**Simple applications in Machine learning (the representer theorem, regression, support vector machines)
**Random variables with values in RKHS, application to dependence (or in-) measures
**A Bayesian point of view

Real Mathematics for Engineers (Prof. Jonathan Manton)
Time & Venue: 1pm to 2pm, Greenwood Theatre, Level 1, EEE Building.