Free Online Machine Learning Course


This is a quick post about a free online course on Machine Learning. The course is run by Andrew Ng at Stanford and I thought it would be of interest to those who read this blog as it covers learning algorithms which help us to understand how humans learn things as well as machines.

The course comes in structured chunks which are released a week at a time. It hasn’t started yet as it is in the pre-launch period but you can go on the site, sign up and watch the first week of videos and answer the review questions to get a head start.

It seems that this course is running as a beta version of what online courses could be in the future. If you’re even slightly interested in how machines, and indeed humans, learn I suggest you sign up and take part. You can set the difficulty as basic or advanced and it’s FREE!

Sign up and see the first week of videos here:

You can also see a lecture series by Andrew Ng on youtube here:

Language Evolution and Language Acquisition

The way children learn language sets the adaptive landscape on which languages evolve.  This is acknowledged by many, but there are few connections between models of language acquisition and models of language Evolution (some exceptions include Yang (2002), Yu & Smith (2007) and Chater & Christiansen (2009)).

However, the chasm between the two fields may be getting smaller, as theories are defined as models which are both more interpretable to the more technically-minded Language Evolutionists and extendible into populations and generations.

Also, strangely, models of word learning have been getting simpler over time.  This may reflect a move from attributing language acquisition to specific mechanisms towards a more general cognitive explanation.  I review some older models here, and a recent publication by Fazly et al.

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