Angel \”Java\” Lopez on Blog

February 21, 2015

Artificial Intelligence: Links And Resources (10)

Filed under: Artificial Intelligence, Links — ajlopez @ 5:40 pm

Previous Post

Stephen Hawking: ‘Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?’ – Science – News – The Independent
http://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence–but-are-we-taking-ai-seriously-enough-9313474.html

Stephen Hawking: Artificial Intelligence ‘Potentially the Worst Thing to Happen to Humanity’ – Yahoo News UK
https://uk.news.yahoo.com/stephen-hawking-artificial-intelligence-potentially-worst-thing-happen-110523390.html#XxZEZ92

NEW SAVANNA: Sheldon Klein on Computing Lévi-Strauss, a blast from the past
http://new-savanna.blogspot.com.ar/2014/05/sheldon-klein-on-computing-levi-strauss.html

IBM unveils a computer that can argue | The Exchange – Yahoo Finance
http://finance.yahoo.com/blogs/the-exchange/ibm-unveils-a-computer-than-can-argue-181228620.html

Does HWO continue (in some way) after finals for 2014? : HWO
http://www.reddit.com/r/HWO/comments/23m8my/does_hwo_continue_in_some_way_after_finals_for/

Hello World Open
https://helloworldopen.com/

The Face Recognition Algorithm That Finally Outperforms Humans — The Physics arXiv Blog — Medium
https://medium.com/the-physics-arxiv-blog/2c567adbf7fc

AI researcher explains how to stop Skynet from happening | Science! | Geek.com
http://www.geek.com/science/ai-researcher-explains-how-to-stop-skynet-from-happening-1591986/

AI Developers to power new generation of context driven artificial intelligence | SiliconANGLE
http://siliconangle.com/blog/2014/04/10/ai-developers-to-power-new-generation-of-context-driven-artificial-intelligence/

The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI | Enterprise | WIRED
http://www.wired.com/2013/05/neuro-artificial-intelligence/all/

pyvideo.org – Exploring Machine Learning with Scikit-learn
http://www.pyvideo.org/video/2561/exploring-machine-learning-with-scikit-learn

Gaussian Processes for Machine Learning: Contents
http://www.gaussianprocess.org/gpml/chapters/

ccv 0.6 open sources near state-of-the-art image classifier under Creative Commons
http://libccv.org/post/ccv-0.6-open-sources-near-state-of-the-art-image-classifier-under-creative-commons/

An AI that mimics our neocortex is taking on the neural networks – and this is how it’ll do it • The Register
http://www.theregister.co.uk/2014/03/29/hawkins_ai_feature/

A review of Her by Ray Kurzweil | KurzweilAI
http://www.kurzweilai.net/a-review-of-her-by-ray-kurzweil

An Introduction to Deep Learning (in Java): From Perceptrons to Deep Networks | Toptal
http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

Why Minds Are Not Like Computers – The New Atlantis
http://www.thenewatlantis.com/publications/why-minds-are-not-like-computers

My Links
http://delicious.com/ajlopez/artificialintelligence

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

February 13, 2015

Artificial Intelligence: Links And Resources (9)

Filed under: Artificial Intelligence, Links — ajlopez @ 7:40 pm

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NessBots – Fighting Robots Code Game
http://www.nessbots.com/welcome/

IBM wants to see your Watson mobile apps | Cloud computing – InfoWorld
http://www.infoworld.com/t/cloud-computing/ibm-wants-see-your-watson-mobile-apps-237230

Why am and eurisko appear to work
http://www.sciencedirect.com/science/article/pii/000437028490016X

Douglas Lenat
http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Douglas_Lenat.html

artificial intelligence – How To Design Eurisko – Stack Overflow
http://stackoverflow.com/questions/2524129/how-to-design-eurisko

Eurisko, The Computer With A Mind Of Its Own | Alicia Patterson Foundation
http://aliciapatterson.org/stories/eurisko-computer-mind-its-own

EURISKO – Lesswrongwiki
http://wiki.lesswrong.com/wiki/EURISKO

Eurisko
http://www.aaai.org/Papers/AAAI/1980/AAAI80-047.pdf

Let’s reimplement EURISKO! – Less Wrong
http://lesswrong.com/lw/10g/lets_reimplement_eurisko/

Eurisko – Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Eurisko

MIT builds intelligent programs for robots that could help with the smart home — Tech News and Analysis
http://gigaom.com/2014/02/12/mit-builds-intelligent-programs-for-robots-that-could-help-with-the-smart-home/

How Google’s Robots Can Learn Like Humans | Fast Company | Business Innovation
http://www.fastcompany.com/3026056/most-innovative-companies-2014/how-googles-robots-can-learn-like-humans

Why Google Is Investing In Deep Learning ⚙ Co.Labs ⚙ code community
http://www.fastcolabs.com/3026423/why-google-is-investing-in-deep-learning

Top 9 Commercial Uses for IBM Watson: Beyond ‘Jeopardy!’ – ABC News
http://abcnews.go.com/Business/top-commercial-ibm-watson-jeopardy/story?id=21477280

YOW! 2013 | Computing Like the Brain: The Path to Machine Intelligence
https://a.confui.com/public/conferences/517fce8207933939cd000001/locations/517fce8207933939cd000002/schedule/topics/51ef9f36950e81dc55000027

PC AI – Forth Programming Language
http://www.pcai.com/web/ai_info/pcai_forth.html

Disguise detection using open source
http://www.aicbt.com/disguise-detection/

Jon’s Place: Roz & RobotsConf
http://blog.huv.com/2013/11/roz-robotsconf.html

aforge – AForge.NET Framework – Google Project Hosting
https://code.google.com/p/aforge/

TURING’S CATHEDRAL | Edge.org
http://www.edge.org/conversation/turing-39s-cathedral

Machine Learning
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

All Models of Learning have Flaws « Machine Learning (Theory)
http://hunch.net/?p=224

SimpleAI (Artificial Intelligence Python lib)
http://vimeo.com/74250290

The Man Who Would Teach Machines to Think – James Somers – The Atlantic
http://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/

Yahoo Acquires Startup LookFlow To Work On Flickr And ‘Deep Learning’ | TechCrunch
http://techcrunch.com/2013/10/23/yahoo-acquires-startup-lookflow-to-work-on-flickr-and-deep-learning/

Google’s Quantum AI Lab adds quantum physics to Minecraft | The Verge
http://www.theverge.com/2013/10/20/4859548/googles-quantum-ai-lab-minecraft-quantum-physics

IBM Watson Cancer – Business Insider
http://www.businessinsider.com/ibm-watson-cancer-2013-10

My Links
http://delicious.com/ajlopez/artificialintelligence

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

February 11, 2015

Machine Learning: Links, News And Resources (8)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 5:02 pm

Previous Post

omphalos/bayesian-bandit.js
https://github.com/omphalos/bayesian-bandit.js

nicolaspanel/node-svm
https://github.com/nicolaspanel/node-svm

yandongliu/learningjs
https://github.com/yandongliu/learningjs

primaryobjects/lda
https://github.com/primaryobjects/lda

tixz/kMeans.js
https://github.com/tixz/kmeans.js

serendipious/nodejs-decision-tree-id3
https://github.com/serendipious/nodejs-decision-tree-id3

tixz/clustering.js
https://github.com/tixz/clustering.js

Encog Machine Learning Framework | Heaton Research
http://www.heatonresearch.com/encog

josephmisiti/awesome-machine-learning
https://github.com/josephmisiti/awesome-machine-learning

Machine learning
https://github.com/showcases/machine-learning

5 ways to add machine learning to Java, JavaScript, and more | InfoWorld
http://www.infoworld.com/article/2608742/predictive-analytics/5-ways-to-add-machine-learning-to-java–javascript–and-more.html

Tryolabs | Python Django development with Artificial Intelligence components |
http://www.tryolabs.com/

lastlegion/linearReg.js
https://github.com/lastlegion/linearReg.js

karpathy/forestjs
https://github.com/karpathy/forestjs

karpathy/svmjs
https://github.com/karpathy/svmjs

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

February 9, 2015

Programando for Internet of Things

Usually, I practice programming every day, applying TDD (Test-Driven Development) workflow. There are non-public projects, but the majority of that practice is public, in my GitHub account. In my opinion, Node.js is a very interesting technology: ubiquos, powerful and simple. I can implement many ideas using Node.js/JavaScript, in a easier way than using other technologies (Java, .NET, Scala, Ruby, Python, Clojure, Smalltalk). The only price to pay: understand and use the JavaScript callbacks.

There are many ideas and implementation, in current applications, public projects, startups. An interesting topic is Internet of Things (OK, a “buzzword”). Many startups are fighting in that battle field. Maybe, many of them will be adquired by Google, Apple, Microsoft. These are interesting time. But this is a time for implement ideas, in open source projects. Then, an startup can leverage the open source world. I think that the important part of an startup is the execution, not the idea or the implementation..

Meanwhile, I want to implement some ideas in my public projects. This post is written to describe the landscape of ideas to implement:

- Collect informaiton from devices connected to the Internet. To have a data repository, the data is send by SDKs, one SDK per device type. Then, use the repository and the collected data in other projects.

- Apply Artificial Intelligence (OK, it is a wide term, but the better we have), to all the collected data, discovering patterns, using machine learning, deep learning. I wrote some JavaScript projects to explore such paths.

- Take decisions, execute actions, using expert systems or other systems. My first implementations, again, are in JavaScript/NodeJs. Having somethink like IFTTT but open source, consuming Internet of Things data.

- Run all these implementations as distributed applicacionts, not only horizontal scalability or cloud computing. Node.js, again, is an interesting vehicle to implement first experiments and applications. Only if needed, then switch to compiled languages and technologies.

I apologize my auto-reference to my projects, but it is the way to explain why I’m writing them.

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 29, 2015

Machine Learning: Links, News And Resources (7)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 6:25 pm

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Machine Learning with Javascript – Ganesh Iyer’s Blog
http://ganeshiyer.net/blog/2013/10/22/machine-learning-with-javascript/

pa7/nude.js
https://github.com/pa7/nude.js

harthur/clusterfck
https://github.com/harthur/clusterfck

harthur/classifier
https://github.com/harthur/classifier

harthur/kittydar
https://github.com/harthur/kittydar

harthur.github.io/kittydar/
http://harthur.github.io/kittydar/

Machine Learning for JavaScript Hackers
http://harthur.github.io/txjs-slides/#1.0

Machine Learning in JavaScript | Hacker News
https://news.ycombinator.com/item?id=7149913

5 ways to add machine learning to Java, JavaScript, and more | Predictive analytics – InfoWorld
http://www.infoworld.com/t/predictive-analytics/5-ways-add-machine-learning-java-javascript-and-more-247535

ConvNetJS: Deep Learning in your browser
http://cs.stanford.edu/people/karpathy/convnetjs/

100 Best GitHub: Artificial Intelligence | Meta-Guide.com
http://meta-guide.com/software-meta-guide/100-best-github-artificial-intelligence/

AppDynamics New Release Brings Big Data and Machine Learning to APM
http://www.infoq.com/news/2014/08/AppDynamics-Release

Machine Learning
http://azure.microsoft.com/en-us/services/machine-learning/

Java Machine Learning | Machine Learning Mastery
http://machinelearningmastery.com/java-machine-learning/

Metacademy – Level-Up Your Machine Learning
http://metacademy.org/roadmaps/cjrd/level-up-your-ml

lauris/awesome-scala
https://github.com/lauris/awesome-scala

Extreme Learning Machines – FastML
http://fastml.com/extreme-learning-machines/

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 24, 2015

Machine Learning: Links, News And Resources (6)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 8:14 pm

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Nuit Blanche: Europe Wide Machine Learning Meetup and Paris Machine Learning #12: Season 1 Finale, Andrew Ng and More…
http://nuit-blanche.blogspot.com.ar/2014/06/europe-wide-machine-learning-meetup-and.html

MojoJolo/textteaser
https://github.com/MojoJolo/textteaser

deeplearning4j.org
http://deeplearning4j.org/

agibsonccc/java-deeplearning
https://github.com/agibsonccc/java-deeplearning

Work on Machine Learning Problems That Matter To You | Machine Learning Mastery
http://machinelearningmastery.com/work-on-machine-learning-problems-that-matter-to-you/

What Does a Neural Network Actually Do? « Some Thoughts on a Mysterious Universe
http://moalquraishi.wordpress.com/2014/05/25/what-does-a-neural-network-actually-do/

Weka 3 – Data Mining with Open Source Machine Learning Software in Java
http://www.cs.waikato.ac.nz/ml/weka/

A Tour of Machine Learning Algorithms | Machine Learning Mastery
http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Speakers and Abstracts | Machine Learning Summer School
http://mlss.soe.ucsc.edu/schedule/speakers

A Primer on Deep Learning | DataRobot
http://www.datarobot.com/blog/a-primer-on-deep-learning/

The People Who Would Teach Machines to Learn | The Official Blog of BigML.com
http://blog.bigml.com/2014/05/19/the-people-who-would-teach-machines-to-learn/

Juan M Gómez’s Blog
http://jmgomez.me//a-fruit-image-classifier-with-python-and-simplecv/

IPAM – Schedule
https://www.ipam.ucla.edu/schedule.aspx?pc=gss2007

Meet the Man Google Hired to Make AI a Reality | Enterprise | WIRED
http://www.wired.com/2014/01/geoffrey-hinton-deep-learning/

Neural networks and a dive into Julia
http://blog.yhathq.com/posts/julia-neural-networks.html

Researchers Teach A Robot To Catch Flying Objects Like Yogi Berra | TechCrunch
http://techcrunch.com/2014/05/12/researchers-teach-a-robot-to-catch-flying-objects-like-yogi-berra/

Machine Learning is Fun! — Medium
https://medium.com/p/80ea3ec3c471

DataMining & MachineLearning
http://paper.li/Karelman/1339006494

Irving Wladawsky-Berger: Why Do We Need Data Science when We’ve Had Statistics for Centuries?
http://blog.irvingwb.com/blog/2014/04/why-do-we-need-data-science-when-weve-had-statistics-for-centuries.html

fergalbyrne/clortex · GitHub
https://github.com/fergalbyrne/clortex

IEPY
http://iepy.machinalis.com/

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 19, 2015

Machine Learning: Links, News And Resources (5)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:53 pm

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Machine Learning in Go using GoLearn | Stephen Whitworth
http://www.sjwhitworth.com/machine-learning-in-go-using-golearn/

mate-tools – Tools for Natural Language Analysis, Generation and Machine Learning – Google Project Hosting
https://code.google.com/p/mate-tools/

CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

lisa-lab/pylearn2
https://github.com/lisa-lab/pylearn2

A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library | Machine Learning Mastery
http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/

Facebook Chases Google’s Deep Learning with New Research Group | MIT Technology Review
http://www.technologyreview.com/news/519411/facebook-launches-advanced-ai-effort-to-find-meaning-in-your-posts/

AI Developers to power new generation of context driven artificial intelligence | SiliconANGLE
http://siliconangle.com/blog/2014/04/10/ai-developers-to-power-new-generation-of-context-driven-artificial-intelligence/

Deep Learning (or not): The why’s have it — FactorialWise
http://factorialwise.com/blog/2014/4/11/deep-learning-or-not-the-whys-have-it

Neural networks and deep learning
http://neuralnetworksanddeeplearning.com/chap2.html

pyvideo.org – Exploring Machine Learning with Scikit-learn
http://www.pyvideo.org/video/2561/exploring-machine-learning-with-scikit-learn

Gaussian Processes for Machine Learning: Contents
http://www.gaussianprocess.org/gpml/chapters/

Machine Learning – complete course notes
http://www.holehouse.org/mlclass/

Learning and Teaching Machine Learning: A Personal Journey
http://www.kdnuggets.com/2014/04/learning-teaching-machine-learning-personal-journey.html

Description – Galaxy Zoo – The Galaxy Challenge | Kaggle
http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge

My solution for the Galaxy Zoo challenge – Sander Dieleman
http://benanne.github.io/2014/04/05/galaxy-zoo.html

Introduction to Information Retrieval
http://www-nlp.stanford.edu/IR-book/

machinalis/featureforge
https://github.com/machinalis/featureforge

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 16, 2015

Machine Learning: Links, News And Resources (4)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 11:10 am

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An Introduction to Deep Learning (in Java): From Perceptrons to Deep Networks | Toptal
http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

Machine Learning & Recommender Systems at Netflix Scale
http://www.infoq.com/presentations/machine-learning-netflix

The Yaksis
http://www.yaksis.com/posts/vowpal_wabbit-the-redis-of-the-data-science-community.html

2014 will be the year you’ll learn Machine Learning — Louis Dorard
http://www.louisdorard.com/blog/2014-machine-learning

Classification with scikit-learn | DataRobot
http://www.datarobot.com/blog/classification-with-scikit-learn/

Machine Learning with Scikit-Learn – Jake Vanderplas on Vimeo
http://vimeo.com/80093925

How Google’s Robots Can Learn Like Humans | Fast Company | Business Innovation
http://www.fastcompany.com/3026056/most-innovative-companies-2014/how-googles-robots-can-learn-like-humans

Machine Learning in Javascript: Introduction | Burak Kanber’s Blog
http://burakkanber.com/blog/machine-learning-in-other-languages-introduction/

Probably Approximately Correct — a Formal Theory of Learning | Math n Programming
http://jeremykun.com/2014/01/02/probably-approximately-correct-a-formal-theory-of-learning/

Machine Learning
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

Seattle area event: A hands-on introduction to machine learning with F# – Visual F# Tools Team Blog – Site Home – MSDN Blogs
http://blogs.msdn.com/b/fsharpteam/archive/2013/11/12/seattle-area-event-a-hands-on-introduction-to-machine-learning-with-f.aspx

Introduction to Machine Learning
http://alex.smola.org/teaching/cmu2013-10-701x/index.html

All Models of Learning have Flaws « Machine Learning (Theory)
http://hunch.net/?p=224

mikeizbicki/HLearn
https://github.com/mikeizbicki/HLearn

The Man Who Would Teach Machines to Think – James Somers – The Atlantic
http://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/

Machine Learning in Python has never been easier – AnalyticBridge
http://www.analyticbridge.com/profiles/blogs/machine-learning-in-python-has-never-been-easier

Code Webs – Visualizing 40,000 student code submissions
http://www.stanford.edu/~jhuang11/research/pubs/moocshop13/codeweb.html

luispedro/BuildingMachineLearningSystemsWithPython
https://github.com/luispedro/BuildingMachineLearningSystemsWithPython

Stanford researchers to open-source model they say has nailed sentiment analysis — Tech News and Analysis
http://gigaom.com/2013/10/03/stanford-researchers-to-open-source-model-they-say-has-nailed-sentiment-analysis/

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 13, 2015

Machine Learning: Links, News And Resources (3)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:19 pm

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Tutorial Slides by Andrew Moore, computer scientist at Google, ex-CMU professor – AnalyticBridge
http://www.analyticbridge.com/forum/topics/tutorial-slides-by-andrew?groupUrl=onlinetutorials

Introduction to Machine Learning | InTechOpen
http://www.intechopen.com/books/theory_and_novel_applications_of_machine_learning

Skills Matter : Progressive F# Tutorials 2013: Matt Moloney
http://skillsmatter.com/podcast/scala/phil-trelford

Deep Learning
http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/

Richard Socher – Deep Learning Tutorial
http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial

tresata/ganitha
https://github.com/tresata/ganitha

Amazon.com: Imbalanced Learning: Foundations, Algorithms, and Applications (9781118074626): Haibo He, Yunqian Ma: Books
http://www.amazon.com/Imbalanced-Learning-Foundations-Algorithms-Applications/dp/1118074629

Peekaboo: Machine Learning Cheat Sheet (for scikit-learn)
http://peekaboo-vision.blogspot.ca/2013/01/machine-learning-cheat-sheet-for-scikit.html

bigdata2013.sciencesconf.org/conference/bigdata2013/pages/bottou.pdf
http://bigdata2013.sciencesconf.org/conference/bigdata2013/pages/bottou.pdf

Análisis Cluster (II): Clasificación no supervisada mediante clasificación jerárquica aglomerativa | Pybonacci
https://pybonacci.wordpress.com/2012/11/19/analisis-cluster-ii-clasificacion-no-supervisada-mediante-clasificacion-jerarquica-aglomerativa/

Building Machine Learning Systems with Python | Meta Rabbit
http://metarabbit.wordpress.com/2013/05/31/building-machine-learning-systems-with-python/

Machine Learning | The F# Software Foundation
http://fsharp.org/machine-learning/

Neural Network Visualisation | Creative Clojure
http://clojurefun.wordpress.com/2013/04/10/neural-network-visualisation/

nuroko/nurokit · GitHub
https://github.com/nuroko/nurokit

Skills Matter : The London Clojure Community:Machine Learnin
http://skillsmatter.com/podcast/java-jee/machine-learning-with-storm-redis/

yods/storm-ml-play · GitHub
https://github.com/yods/storm-ml-play

Manning: Machine Learning in Action
http://www.manning.com/pharrington/

scalanlp/nak · GitHub
https://github.com/scalanlp/nak

Cornell Chronicle: Student’s research could shake up Wall Street
http://www.news.cornell.edu/stories/March13/Zvorinji.html

A Tutorial on Learning With Bayesian Networks – Microsoft Research
http://research.microsoft.com/apps/pubs/default.aspx?id=69588

Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part One) | The Official Blog of BigML.com
http://blog.bigml.com/2013/02/15/everything-you-wanted-to-know-about-machine-learning-but-were-too-afraid-to-ask-part-one/

Resources « CS 194-16: Introduction to Data Science
http://datascienc.es/resources/

harthur/brain · GitHub
https://github.com/harthur/brain

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

January 12, 2015

Machine Learning: Links, News And Resources (2)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 9:27 am

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CS 229 Machine Learning Course Materials
http://cs229.stanford.edu/materials.html

Will it Python? Machine Learning for Hackers, Chapter 2, Part 2: Logistic regression with statsmodels
http://slendrmeans.wordpress.com/2012/12/21/458/

Foundations Of Machine Learning
http://mitpress.mit.edu/books/foundations-machine-learning-0

How I made $500k with machine learning and HFT (high frequency trading)
http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft

jimpil / enclog
https://github.com/jimpil/enclog
Clojure wrapper for Encog (v3) (Machine-Learning framework that specialises in neural-nets)

Machine Learning: Genetic Algorithms in Javascript Part 2
http://burakkanber.com/blog/machine-learning-genetic-algorithms-in-javascript-part-2/

5 Principles for Applying Machine Learning Techniques
http://blog.factual.com/5-principles-for-applying-machine-learning-techniques

Understanding the Bias-Variance Tradeoff
http://scott.fortmann-roe.com/docs/BiasVariance.html

Up And Running With Python – My First Kaggle Entry
http://blog.kaggle.com/2012/07/02/up-and-running-with-python-my-first-kaggle-entry/

Machine Learning for Hackers
http://www.johndcook.com/blog/2012/03/07/machine-learning-for-hackers/

8 Crazy Things IBM Scientists Have Learned Studying Twitter
http://www.businessinsider.com/8-crazy-things-ibm-scientists-have-learned-studying-twitter-2012-1

What have been the most interesting papers in computer science for 2011?
http://www.quora.com/What-have-been-the-most-interesting-papers-in-computer-science-for-2011

Infer.NET
http://research.microsoft.com/en-us/projects/infernet/
Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is currently downloadable as a beta release under a non-commercial license.

Machine Learning
http://area51.stackexchange.com/proposals/26434/machine-learning

Hadoop and Machine Learning
http://www.slideshare.net/joshwills/hadoop-and-machine-learning

Machine Learning, Hadoop, and Mahout
http://nosql.mypopescu.com/post/14559975263/machine-learning-hadoop-and-mahout

Bayesian Reasoning and Machine Learning
http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

Enbracing Uncertainty
http://embracinguncertainty.info/

The New Machine Intelligence
http://scpro.streamuk.com/uk/player/Default.aspx?wid=7739

Machine learning for dummies
http://blogs.technet.com/b/next/archive/2011/02/16/machine-learning-for-dummies-john-platt.aspx

Smart Data Structures: An Online Machine Learning
Approach to Multicore Data Structures
http://groups.csail.mit.edu/carbon/wordpress/wp-content/uploads/2011/03/eastep-smart-data-structures-icac11.pdf

Best Paper Awards in Computer Science (since 1996)
http://jeffhuang.com/best_paper_awards.html

Review of 2011 free Stanford online classes
http://programming-puzzler.blogspot.com.ar/2011/11/review-of-2011-free-stanford-online.html

My Links
http://delicious.com/ajlopez/machinelearning

Stay tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

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