Machine Learning - Intro and Gradient Descent

Week 1 - In which we meet Prof. Ng and experience the delights of cost functions and Linear Regression with One Variable.

This is my first week on the Stanford University machine learning course and so far so good -as the man said when he jumped off Nelson's column. Prof. Ng seems to be a pretty decent lecturer who doesn't over estimate the ability of his student and there are plenty of examples and explanations to drive the points home. The lectures come as videos with a few embedded questions to keep you awake, and they are, thankfully, split into bite sized chunks of 10 -15 minutes.

In the intro we get introduced to terminology -the difference between supervised and unsupervised learning. Supervised learning requires a training set, the main  example given is of house price history vs. house size; unsupervised is just trying to make inferences from a bunch of data -say clustering news stories. We are also given the difference between regression (line fitting, trending) problems and classification ones (sorting into buckets, true/false &c.).

Thence we arrive at Linear Regression with One Variable -which is line fitting on a 2D graph basically. The maths arrives, but not too brutally -calculus shows its head, but fortunately you don't have to understand the whole of calculus to use the little bit we want, so all good.

As well as the videos there are also online review questions -which I believe contribute to the final score. The good news is that you not only can, but are actively encouraged to, retake them until you get a perfect score. The real benefit of this is that they become a learning tool rather than just a test, there are answers given as well as just scores and I think that they have helped me understand what's going on rather better than I otherwise would.

I'm now most of the way through the optional linear algebra review -but I won't post on that.

Free university courses -computing at Stanford

This is a bit of a find, free Artificial Intelligence. and Machine Learning courses at Stanford University in the US. These two courses are examined (optionally, in the Advanced Track) and you can get a certificate, but there are other lecture series on more 'normal' programming topics too.

I think the 'Seven Languages in Seven Weeks' is going to take a bit of a knock as I have signed up for both AI and ML courses, neither have officially started yet but the first weeks' lectures are available for for machine learning.

I'll post on how my attempts at this go -suffice to say week one reveals the course to be fairly academic; and a friend is struggling with the mathematical approach, more because he finds it off-putting rather than difficult. I'm no Turing but so far so good -and there's always Khan Academy to fill in the gaps.

Khan Academy -learning for all.

What a great idea! The Khan Academy is a teaching site based around videos hosted on youtube and interactive exercises. There are 1800 plus videos, mainly secondary school maths and science at the moment, but Sal Khan hopes to push it out to other areas with time. The exercises are pretty much all maths -except for some on projectile motion.

I have been cracking through the maths exercises trying to find my level -probably around the third year -or year ten in new money. With the school shut for snow I put my 11 year old in front of the site, and she didn't run away screaming. I think that's partly due to the points system, competititive little begger wants a high score.

The maths videos range from simple addition up to moderate calculus so I'd recommend it to anyone looking to brush up their skills or who needs to supplement what their kids are learning in school. Sal is an engaging lecturer, who doesn't force the pace, and he is endearingly bad at using the drawing tools. His explanations are pretty clear and timed well to fit in with the ten minute maximum time for youtube.

There is an exercise map (now in Google Maps -interesting idea), showing how the topics join up, and you are suggested new exercises as you complete old ones. Success here means getting ten answers right in a row, for me this led to me finding my old scientific calculator and checking answers before submitting them, as getting number 9 wrong can lead to the computer hitting the wall. There is also a hints mechanism that works the question through with you -but using it means that you break your streak (or run of right answers), so if you are unsure you may be better off going to the linked video for an explanation.

Probability and Stats for me next I think.