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Information on coursework, recommended reading, etc, in pdf. to:
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Assignments in 2016
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We are using the [IP 2016?:2016 version] of the course notes written by Sze M Tan, Colin Fox and Geoff K. Nicholls. to:
We are using the 2016 version of the course notes written by Sze M Tan, Colin Fox and Geoff K. Nicholls. Changed line 17 from:
We are using the Attach:2016 Δ version of the course notes written by Sze M Tan][, [[http://elec.otago.ac.nz/w/index.php/Colin_Fox|Colin Fox and Geoff K. Nicholls. to:
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Course Material in Electronic FormWe are still using the 2009 version of the course notes written by [http://home.comcast.net/~SzeMengTan/ Sze M Tan], Colin Fox? and [http://www.stats.ox.ac.uk/~nicholls/ Geoff K. Nicholls]. Colin intends (!) to update these notes: updates will be announced as they become available. to:
Course Material in Electronic FormWe are using the Attach:2016 Δ version of the course notes written by Sze M Tan][, [[http://elec.otago.ac.nz/w/index.php/Colin_Fox|Colin Fox and Geoff K. Nicholls. Added lines 10-19:
Study Guide:Information on coursework, recommended reading, etc, in pdf. Course Material in Electronic FormWe are still using the 2009 version of the course notes written by [http://home.comcast.net/~SzeMengTan/ Sze M Tan], Colin Fox? and [http://www.stats.ox.ac.uk/~nicholls/ Geoff K. Nicholls]. Colin intends (!) to update these notes: updates will be announced as they become available. Course Notes:Changed lines 9-11 from:
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There are no specific prerequisites, however ELEC 441 is strongly recommended preparation. Papers in analysis of experiments, probability, inference, and programming will also be useful preparation for ELEC 445. Added lines 1-11:
(:notitle:) ELEC 445: Inverse Problems and ImagingClassical and modern solution methods for inverse problems including image de-blurring and analysis of experimental data. An introduction to inverse problems that arise in mathematical descriptions of physical systems, including image deblurring and analysis of data from experiments. Covers analytic and computational tools for solving inverse problems, based on classical regularization methods and Bayesian inference, as well as theoretical properties of inverse problems and solutions. Prerequisites: None |