Media Summary: Seeing is believing - Dr Mike Pound helps us understand how to turn our datapoints into Powerpoints. This is part 2 of the If you run out of headroom with your chosen sample rate, how do you avoid the problems of unwanted harmonics? Search Engines are a bit like the Public Library - You wouldn't wander around hoping to find the book you want, there's a system ...

Sensemaking Data Visualisation Computerphile - Detailed Analysis & Overview

Seeing is believing - Dr Mike Pound helps us understand how to turn our datapoints into Powerpoints. This is part 2 of the If you run out of headroom with your chosen sample rate, how do you avoid the problems of unwanted harmonics? Search Engines are a bit like the Public Library - You wouldn't wander around hoping to find the book you want, there's a system ... If you're not the customer you are the product. Dr Max Wilson on the third party apps embedded in social media. EXTRA BITS: ... Dicussing implementation with Professor Brailsford. Professor Brailsford emailed me after we recorded this to say that of course ... Researchers stumbled upon a simple but worrying bug. Cropped images from Pixel phones contained a great deal of the original ...

We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains ... A clean sweep. How to get rid of the unnecessary clutter in your Bayesian logic is already helping to improve Machine Learning results using statistical models. Professor Mike Osborne drew us ... SGML 'theologians' were at war with Internet browser 'pragmatists' after Sir Tim Berners-Lee released HTML on the world. After a recent collaboration with an artist, Professor Moriarty is exploring whether the physics within patterns and art can be ... Language Models' Achilles heel: Rob Miles talks about "glitch" tokens, those mysterious words which, which result in gibberish ...

Photo Gallery

SenseMaking (Data Visualisation) - Computerphile
Foundations of Data Visualisation - Computerphile
Data Analysis 2: Data Visualisation - Computerphile
Oversampling Data (Explained with Audio) - Computerphile
How Search Engines Treat Data - Computerphile
Social Media Data - Computerphile
Implementation - Computerphile
Data Analysis - Computerphile
Acropalypse Now - Computerphile
Machine Learning Methods - Computerphile
Data Analysis 3: Cleaning Data - Computerphile
Using Bayesian Approaches & Sausage Plots to Improve Machine Learning - Computerphile
View Detailed Profile
SenseMaking (Data Visualisation) - Computerphile

SenseMaking (Data Visualisation) - Computerphile

Turning

Foundations of Data Visualisation - Computerphile

Foundations of Data Visualisation - Computerphile

Following a look at '

Data Analysis 2: Data Visualisation - Computerphile

Data Analysis 2: Data Visualisation - Computerphile

Seeing is believing - Dr Mike Pound helps us understand how to turn our datapoints into Powerpoints. This is part 2 of the

Oversampling Data (Explained with Audio) - Computerphile

Oversampling Data (Explained with Audio) - Computerphile

If you run out of headroom with your chosen sample rate, how do you avoid the problems of unwanted harmonics?

How Search Engines Treat Data - Computerphile

How Search Engines Treat Data - Computerphile

Search Engines are a bit like the Public Library - You wouldn't wander around hoping to find the book you want, there's a system ...

Social Media Data - Computerphile

Social Media Data - Computerphile

If you're not the customer you are the product. Dr Max Wilson on the third party apps embedded in social media. EXTRA BITS: ...

Implementation - Computerphile

Implementation - Computerphile

Dicussing implementation with Professor Brailsford. Professor Brailsford emailed me after we recorded this to say that of course ...

Data Analysis - Computerphile

Data Analysis - Computerphile

Dr Mike Pound introduces a ten videos on

Acropalypse Now - Computerphile

Acropalypse Now - Computerphile

Researchers stumbled upon a simple but worrying bug. Cropped images from Pixel phones contained a great deal of the original ...

Machine Learning Methods - Computerphile

Machine Learning Methods - Computerphile

We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains ...

Data Analysis 3: Cleaning Data - Computerphile

Data Analysis 3: Cleaning Data - Computerphile

A clean sweep. How to get rid of the unnecessary clutter in your

Using Bayesian Approaches & Sausage Plots to Improve Machine Learning - Computerphile

Using Bayesian Approaches & Sausage Plots to Improve Machine Learning - Computerphile

Bayesian logic is already helping to improve Machine Learning results using statistical models. Professor Mike Osborne drew us ...

Data Analysis 8: Classifying Data - Computerphile

Data Analysis 8: Classifying Data - Computerphile

For your eyes only! Classifying

HTML: Poison or Panacea? (HTML Part2) - Computerphile

HTML: Poison or Panacea? (HTML Part2) - Computerphile

SGML 'theologians' were at war with Internet browser 'pragmatists' after Sir Tim Berners-Lee released HTML on the world.

Computing With Art - Computerphile

Computing With Art - Computerphile

After a recent collaboration with an artist, Professor Moriarty is exploring whether the physics within patterns and art can be ...

Glitch Tokens - Computerphile

Glitch Tokens - Computerphile

Language Models' Achilles heel: Rob Miles talks about "glitch" tokens, those mysterious words which, which result in gibberish ...

Effects of visualization and note-taking on sensemaking and analysis

Effects of visualization and note-taking on sensemaking and analysis

Full Title: Effects of