Artificial Intelligence

Why This, Why Now…

22/05/2016 By debkr

goal = 'career-change'
reason = 'https://twitter.com/Qwiery/status/727849124138192896'

# PTP: Programming Stream (url = 'http://deborahroberts.info/2016/02/diving-into-data-syllabus-2/')
longlist = ['Java', 'Python', 'SQL', 'VBA', 'R']
start_with_end_in_mind = {'datascience': ['R', 'Python'], 'machinelearning': ['Python', 'R']}
choice1 = start_with_end_in_mind.get('datascience')
choice2 = start_with_end_in_mind.get('machinelearning')

shortlist = list()
for item in choice2 :
    if item in choice1  :  shortlist.append(item)

quit()

# Due to unorthodoxy I executed the last line just after line 02

https://twitter.com/Qwiery/status/…

http://deborahroberts.info/2016/02/diving-into-data-syllabus-2/

Ed Tech PTP: Summary Syllabus

03/05/2016 By debkr

My newly-established EdTech Personalised Training Plan includes the following streams:

1. Information Theory Stream
2. Learning/Development Stream: how humans learn vs. how machines learn + developing machines to augment human learning
3. Collaborative Learning Stream
4. Innovation Stream
5. Agile Methodologies Stream
6. Educational Technology Stream: to review the technological and presentational innovations shaping education and L&D

Refer my earlier post “Learning About Learning” for some background on this new/additional PTP.

Ed Tech Resources (work in progress)

03/05/2016 By debkr

Below is a list of resources for the Learning/Development Stream of my newly-established EdTech Personalised Training Plan (this list is a work-in-progress).

Courses:
Learning to Learn module on Coursera, Univ. of California, San Diego

Learning About Learning

03/05/2016 By debkr

As I’ve been going along in this mission of discovery that is my mid-life career-change search, I’ve come to realise just how important Learning & Development really is to me. It’s become a key area for me to look further into – especially in response to my wholly-positive experiences of MOOC’s so far and in relation to my budding interest in all things programming- and technology-led.

I’ve been thinking more and more about the ideas Accelerated Learning and Augmented Learning (specifically in relation to an ongoing developmental Programming Project). In brief, this is about how machine learning and artificial intelligence can be used to help and empower humans, rather than just be there to make rich people more money and put more people out of jobs and into long-term unemployment or underemployment. [Read more…] about Learning About Learning

Diving Into Data: Course Listing (work in progress)

28/02/2016 By debkr

Stanford Engineering Everywhere
URL: https://see.stanford.edu/Course

Computer Science/Programming: Programming Methodology; Programming Abstractions; Programming Paradigms

Artificial Intelligence: Introduction to Robotics; Machine Learning [Read more…] about Diving Into Data: Course Listing (work in progress)

Resources: Artificial Intelligence / Machine Learning (work in progress)

27/02/2016 By debkr

Universities
Stanford Computer Science (AI Research): http://cs.stanford.edu/research/ai
Stanford AI Lab: http://ai.stanford.edu/
Stanford Search: http://www.stanford.edu/search/
Stanford AI100: One Hundred Year Study on Artificial Intelligence

Articles
Why big tech companies are open-sourcing their AI systems
How many computers does it take to identify a cat?

People
Andrew Ng http://www.andrewng.org/
Yaser S. Abu-Mostafa http://work.caltech.edu/
See also my post here (various sources as given): Most Influential Data Scientists (Two Lists) (includes Machine Learning)
Dr. Francois M. Vanderseypen: https://github.com/Orbifold/ \\ http://www.orbifold.net/ \\

Software
TensorFlow https://www.tensorflow.org/ (Google says: “we believe [TensorFlow] to be one of the best machine learning toolboxes in the world, we hope to create an open standard for exchanging research ideas and putting machine learning in products. Google engineers really do use TensorFlow in user-facing products and services, and our research group intends to share TensorFlow implementations along side many of our research publications.”) (includes ‘Get Started’ and ‘Tutorials’ sections.)
Orbifold ML algos on GitHub: https://github.com/Orbifold/XAct

Learning: Recommended Courses
Article: How do I learn machine learning?
Article: 7 Free Machine Learning Courses

Glossaries
Machine Learning – Glossary of Terms, Stanford Artificial Intelligence Lab
Machine Learning Dictionary, School of Computer Science and Engineering, University of New South Wales

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