ai

General AI including ML  (not assessment, text mining and text classification, etc. related)   aka aifc aes                OMG:   Textio  job

e.g. Expert Systems and ML applied to vision, translation, etc.
  
See also:  HAL archive here  (requires access permission)
 
AI for Medicine; ML for Medicine; Deep Learning for Medicine: all HERE   blog / ML explained here

http://datascience.uci.edu  data science at UCI  Smyth  AES  ML  RapidMiner summary of open source data libraries attached.
includes 


 

Amazon Machine Learning (HERE)

 
Open Source AI & Machine Learning 

 
http://ankara.lti.cs.cmu.edu/side/
LightSIDE from CMU:   

It is available here:   http://ankara.lti.cs.cmu.edu/side/ 
source here
http://ankara.lti.cs.cmu.edu/side/

google group here
 
Alchemy: 

http://alchemy.cs.washington.edu


 

www.rapidminer.com


RapidMiner:
lots more here

rapidminer






(now open source Apache) 
 
Microsoft - Azure Machine Learning Studio
 
www.continuum.com


Google - TensorFlow 

TensorFlow

 
Facebook -  AI / ML to open source 
Torch project
https://github.com/torch/torch7


 

The DALMOOC on EdX
https://linkresearchlab.org/dalmooc/

and BDE on EdX (archived):
(big data in education)

and

Ryan Baker's Courses both here and

 

 Machine Learning at CMU

 Ryan Shaun Baker PhD
Alchemy is funded by: 



 

Textio address:


1218 3rd Ave #1900, Seattle, WA 98101




 
It's interesting to see that General Electric developed their Data Science and Machine Learning capabilities through acqui hiring. SmartSignal, Wise,io, and Bit Stew are three that I've heard about.




http://sites.uci.edu/automatedreasoninggroup/       automated reasoning group

vision.caltech.edu           latent dirichlet analysis   Ryszard S. Michalski

ImageNet:    http://www.image-net.org here

http://www-cs.stanford.edu/content/imagenet-large-scale-hierarchical-image-database

Google Open Source AI:   TensorFlow here

Padhraic Smyth's talk 151111:

the master algorithm:  pedro domingos... book... a bit of hype... but a good book.

bits / dollar on a logarithmic scale (vertical) x-axis time / years....

factor in sensors, storage, computation power, analysis methods, and internet? 

before 1994 -- pre-internet.  can collect and share data...much more easily.  

facebook: friendship graph - at 500 million users, 130 average edges per node, 60 billion edges... 

3 billion photos per month, 30 billion content items shared every month.

Face detection now mostly a solved problem -- due to amount of data, compute power, etc.  

Lots of data from Large Hadron Collider at CERN too.  Daniel Whiteson, Physics dept. 

Tensor Flow / ACM learning seminar   Title: TensorFlow: A Framework for Scalable Machine Learning

Date: Wednesday, October 19, 2016

Time: 12:00 PM Eastern Daylight Time

Duration: 1 hour

Test your computer to make sure you meet the minimum technical requirements.

Test Your System: http://event.on24.com/utils/test/testYourSystem.html?eventid=1244624&sessionid=1&key=286042D537A45A41F7ECC947A3D2C193&checkBrowser=true&checkOS=true&checkBandwidth=true&checkCookie=true&ngwebcast=true&ngwebcast=true

 

https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7

Copy of Medium post about key Python Data Libraries for decision science, visualization, machine learning, deep learning, NLP and data mining / statistics is attached.


Top 15 Python Libraries for Data Science in 2017 ............................................................................. 1

I. Core Libraries. .......................................................................................................................... 2

1. NumPy (Commits: 15980, Contributors: 522) ...................................................................................................................... 2

2. SciPy (Commits: 17213, Contributors: 489) .......................................................................................................................... 2

3. Pandas (Commits: 15089, Contributors: 762) ...................................................................................................................... 3

II. Visualization. ........................................................................................................................... 5

4.Matplotlib (Commits: 21754, Contributors: 588) ................................................................................................................. 5

5. Seaborn (Commits: 1699, Contributors: 71) ......................................................................................................................... 6

6. Bokeh (Commits: 15724, Contributors: 223) ........................................................................................................................ 7

7. Plotly (Commits: 2486, Contributors: 33) .............................................................................................................................. 8

III. Machine Learning. ................................................................................................................ 9

8. SciKit-Learn (Commits: 21793, Contributors: 842) ........................................................................................................... 9

IV. Deep Learning — Keras / TensorFlow / Theano .................................................................... 10

9.Theano. (Commits: 25870, Contributors: 300) ................................................................................................................... 10

10. TensorFlow. (Commits: 16785, Contributors: 795) ...................................................................................................... 11

11. Keras. (Commits: 3519, Contributors: 428) ..................................................................................................................... 12

V. Natural Language Processing. ................................................................................................. 13

12. NLTK (Commits: 12449, Contributors: 196) .................................................................................................................... 13

13. Gensim (Commits: 2878, Contributors: 179) ................................................................................................................... 14

VI. Data Mining. Statistics. ....................................................................................................... 15

14. Scrapy (Commits: 6325, Contributors: 243) .................................................................................................................... 15

15. Statsmodels (Commits: 8960, Contributors: 119) ......................................................................................................... 15

VII. Conclusions. ........................................................................................................................ 16