Friday, November 16, 2018

Principles of Communications, 2018/2019 - Week "4" 12,14,16 Nov 2018

Control Theory:- see notes here

Original Paper by James Clerk Maxwell!

on governors basically for steam engines

Friday, November 09, 2018

Principles of Communications, 2018/2019 - Week "3" 5,7,9 Nov 2018


This week, we covered
multicast, mobile, and meanderandom routing, and 
made a very brief start on flow control, just dealing with open loop (call setup/admission control and parsimonious flow descriptors).

Tuesday, November 06, 2018

corporate/brand top level domains

was teaching about internet standards process today, and was asked about gTLDs being handed out to the likes of Apple etc - here's an interesting set of data about the success of that process - it seems also to have caused some political debate:-)

Friday, November 02, 2018

Principles of Communications, 2018/2019 - Week "2" 29.10, 31.10, 2.11

Covering BGP core attrbiutes&hacks, model, and performance.

Why might you be interested in BGP, today:
just one traffic redirection/interception story


Next week, multicast and random routing

Friday, October 26, 2018

Principles of Communications, 2018/2019 - Week "1" 24&26.10.18

This week, course started with brief Intro (what's in course)

Then skip to last lecture on:
Systems (will revisit at end of term)

Then:
Routing 1 - intro + fibbing (hybrid central/distributed inter-as routing)

Monday 29th, will start on BGP...

Monday, October 22, 2018

white space graphs

we often form graphs by addings edges to a collection of vertices, or vertices and edges - we can also form graphs by moving (rewiring) ends of edges from one node to another, or removing edges and nodes.

how about we form graphs by cutting holes in a piece of paper (or by dropping polygonal shapes on a surface)? they can overlap....

what sort of graphs do you get when you just make them out of interstial space?

Wednesday, October 17, 2018

Public Understanding of Machine Learning

"I understand we understand one another" is a great line from the Philadelphia Story - one fab film (also made as a great musical).

So machines start to understand us, at least statistically, programmes create models that may or may not have predictive value about our behaviour (film viewing, travel preferences, dating, etc etc).

But do we understand the machines (or do we, as Pat Cadigan said in the prescient novel Synners, need to "change for the machines"? (including 50 pence bits, as she implied :-)

David Spiegalhalter has spent a lot of time working out how to convey Uncertainty to the non-technical person. Now we have a more complex task

not just mean, variance, confidence limits etc, but also

principle components
linear regression
k-means
bayes
random forest
variational approaches
convolutional neural networks
GANs

etc etc

What hope have we for this? What fear do we have of that? All is blish, all is blush, all is plash.