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Dr. Christopher Bishop will be giving the Turing Lecture this year on the topic of Machine Learning.

Dr. Bishop is a highly respected figure in the Machine Learning discipline and wrote Pattern Recognition and Machine Learning, a great place to start if you’re interested in the subject. It’s certainly on my bookshelf.

He’s giving the lecture in London, Cardiff, Edinburgh and Manchester, and Manchester’s lecture is on the 17th March.

Paul Brabban Machine Learning, University of Manchester

I’ve seen so much awesome stuff in my forays into Machine Learning as part of the course I’m doing, I thought I’d present for your entertainment and information my top 5 machine learning resources.

No, come back – some of this stuff is actually quite cool, I promise!

Here goes, in no particular order:

How to make $1M with Machine Learning and Netflix

Netfix offered a $1M prize for a team that could best their video classification technology by a significant margin.
The netflixprize page tells the official story, and the presentation attached to this blog post is well worth a look.

Detexify – Handwritten symbol recognition

For those of you that use LaTeX, you’ll know the pain of trying to find the code for a symbol if you can’t remember it. Detexify lets you draw a symbol on the screen, then uses machine learning techniques to recognise it and tell you which codes it thinks you need. The accuracy is astonishing – a really good showcase for the potential of the techniques.

Detexify in action

Lego Mindstorms Robots that Learn

This JavaWorld article takes Lego Mindstorms and adds a pinch of Machine Learning to make a robot that learns to follow a path on the ground.

I highly recommend this article for a casual read, it’s very nicely written and accessible but does delve into the theory and mathematical foundations of the Perceptron algorithm at the heart of the article.

Machine Learning at videolectures.net

There are 794 presentations and lectures – that’s not a typo, seven hundred and ninety-four – on every aspect of machine learning you could dream of here, at videolectures.net, from a range of sources. Many are quite approachable for the layperson.

The Singularity Summit

To wrap up, the Singularity Summit seems to be the forum for the players in the general Artificial Intelligence arena to talk about the past, future and philosophical implications of AI.

The Conversations Network hosts a free podcast series for the summit – personally, I really enjoyed James Hughes’ twenty-odd minute talk, in which he answers one of  the great unanswered questions – if you’re standing on a railway bridge, are you safer stood next to an artificial intelligence or a human being?

That’s All Folks

I hope there’s something in there that’s given you some food for thought. If you have any stuff that you think is awesomely cool in this space, drop me a comment so I can check it out!

Paul Brabban Computer Science, MSc, Machine Learning

So that’s the end of the taught course in Machine Learning, finishing up learning about Markov Chains and Hidden Markov Models.

Yep, those are just links to the Wikipedia articles, and it’s quite possible that if you clicked on them and you’re anything like me, the crazy-looking maths stuff at the other end made the sensible part of your brain run off and sit in a corner, clutching its knees and rocking gently back and forth.

Probably muttering to itself.

To be honest, I can’t really explain what this stuff is about just yet – I’ve had a lot crammed into my head over the past few weeks, and I think I need a really good night’s sleep before I can comprehend the deeper aspects of this last bit. Suffice to say for now that it seems like some really interesting and powerful ideas are in play, and when I’ve got my head round it I’ll blog up my thoughts.

I’ve now got one more homework assignment on today’s material to complete by next Wednesday, and the project we’ve been assigned to do is then due on Friday 6th November – a nice surprise, as a typo on the schedule had us believe it was due on the previous Tuesday.

I’m sorry the taught part of the course is done, to be honest. Although I’m not sure I could have taken any more at the pace it was being taught, I’ve thoroughly enjoyed the material.

In fact, I’d say I feel a little inspired.

And, as James Brown might say – it feels good.

Paul Brabban Computer Science, MSc, Machine Learning