
The subtitles/captions on this talk are being edited. They
will be available within 2 weeks of the talk being published.24 February 2021Simon RicheTitle:
Hecke action on the principal block of…
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English Date Created
February 24th, 2021


We work through how to apply largesample theory to A/B testing.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 28th, 2021


We explain the principle of A/B testing, and how to derive confidence intervals for the difference in population proportions using the bootstrap.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 28th, 2021


We extend logistic regression to multiple independent variables, and show how we can use the boostrap to estimate confidence intervals for coefficients, and to test hypotheses.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial No Derivatives Language
English Date Created
January 20th, 2021


We'll look at means from small samples, where the normal approximation of the sampling distribution of the mean breaks down.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial No Derivatives Language
English Date Created
January 15th, 2021


We give a formal definition of confidence intervals, and show how the random interval can be converted to a random variable
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 15th, 2021


We introduce the standard error of an estimator and, in particular, the standard error of the mean.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 14th, 2021


We'll introduce the concept of bias and variance of estimators.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 14th, 2021


We'll introduce the concept of estimating a population or distribution parameter from a sample.
Course Code
INFR08030 Publisher
David Sterratt Licence Type
Creative Commons  Attribution Non Commercial Language
English Date Created
January 14th, 2021


In this video, the concepts in estimation theory introduced so far for
scalar random variables are extended to deal with estimating multiple
parameters, for example the mean and variance of a…
Course Code
PGEE11164 Licence Type
All rights reserved Date Created
October 25th, 2020


In this video, the question of measuring and quantifying the performance
of an estimator is discussed. The video focusses on the concepts and
definitions of bias and variance of the probability…
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PGEE11164 Licence Type
All rights reserved Date Created
October 22nd, 2020


In this video, Estimation Theory is introduced in which unknown
parameters are estimated from data, rather than assuming that problems
can be described by fully known distributions or statistics.…
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PGEE11164 Licence Type
All rights reserved Date Created
October 19th, 2020


Since linear transformations is such an important class of signal
processing systems, this video looks at considering linear
transformations of random vectors. After discussing various types of…
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PGEE11164 Licence Type
All rights reserved Date Created
October 12th, 2020


Auxiliary variables are introduced as a method for calculating a single
function of multiple random variables, through a twostage process of
using the probability transformation rule followed by…
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PGEE11164 Licence Type
All rights reserved Date Created
October 7th, 2020


Preliminaries  perceptrons
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Breaking ties between nearest neighbors
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