Within Automotive there are news everyday about new accomplishmens. Like from Audi, Mercedes, Telsa and all the others. Also IT gigants like Google and open-secretly Apple as well as often critizised Uber.

Even there are many restrictions often commented – mostly within the areas of ethics and laws – it’s a clear trend towards autonomous driving with more and more driver assistance systems.

Already for me, as digital native, the software features running within a car trumps the design & interrieur etc.

Working within the Automotive area on machine learning and artificial intelligence means having great changes ahead.

So more will follow

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The lecturers will be Pieter Abbeel and Dan Klein, which both have masses of very positive student reviews and previous experience.

Artificial Intelligence | edX.

Stay also tuned for further information as well as lecture notes and possible discussions about this course on ai-claudio.com. If you can’t wait, have a look at UDACITY Machine Learning – 1 Supervised.

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Google blogs about it’s research about image processing wich feeds int natural language processing – sounds quite interesting as they using  Recurrent Neural Network (RNN).

Interesting that that value cohesion that high – as Claudio’s first little Open Source together with C. Tragl was called ‘Little Cohesian Helper’ – to analsye speeches.

via Research Blog: A picture is worth a thousand coherent words: building a natural description of images.

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At ai-claudio we have several thoughts on how to combine both of them with common tools, such as NLTK, wordnet, …

Here is someone elses project to do so:

https://github.com/numenta/nupic/wiki/Natural-Language-Processing

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Slideshare: OpenML Tutorial: Networked Science in Machine Learning.

The Open Machine Leraning Plattform tries to improve machine learning through combined research.

See: http://openml.com

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They are hold by

Michael Littman, Professor of Computer Science at Brown University and

Charles Isbell, Professor and Senior Associate Dean at the School of Interactive Computing at Georgia Tech

[jwplayer player=”1″ mediaid=”82″]

The first part is giving a short introduction, some definitions

e.g. if  (Machine Learning) ML is considered to be “computational statistics” (MLitt) or a “broader notion of building computational artefacts that learn over time based on experience” (CI).

Generally it’s said, that Supervised Learning is Functional Approximation: Generalisation, Inductive Bias -> Deduction aka ‘Reasoning’.

Unsupervised Learning: has not those input, therefore creating an own structure and classes to create a compact Description of the data sets.

Reinforcement Learning is Learning from delayed reward, even if it will still be unclear what exactly was the mistake (e.g. at a lost tick tack toe game).

They are going to show Tools and Techniques.
And talk about : supervised learning: labels data well –  reinforcement learning: behaviour scores well – unsupervised learning: cluster scores well.

http://www.udacity.com/course/ud675 preview – (www) 

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