Tag: research

Occupy the Internet

BORN IN THE USA

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Free Machine Learning at Stanford

One of the cornerstones of the technology revolution, Stanford now offers some courses for free via distance learning. One of the more exciting courses is of course- machine learning

 

 

http://jan2012.ml-class.org/

About The Course

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

The Instructor

Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.

 

  1. When does the class start?The class will start in January 2012 and will last approximately ten weeks.
  2. What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
  3. Will the text of the lectures be available?We hope to transcribe the lectures into text to make them more accessible for those not fluent in English. Stay tuned.
  4. Do I need to watch the lectures live?No. You can watch the lectures at your leisure.
  5. Can online students ask questions and/or contact the professor?Yes, but not directly There is a Q&A forum in which students rank questions and answers, so that the most important questions and the best answers bubble to the top. Teaching staff will monitor these forums, so that important questions not answered by other students can be addressed.
  6. Will other Stanford resources be available to online students?No.
  7. How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
  8. Do I need to buy a textbook for the course?No.
  9. How much does it cost to take the course?Nothing: it’s free!
  10. Will I get university credit for taking this course?No.Interested in learning machine learning-

    Well here is the website to enroll http://jan2012.ml-class.org/

Interview Zach Goldberg, Google Prediction API

Here is an interview with Zach Goldberg, who is the product manager of Google Prediction API, the next generation machine learning analytics-as-an-api service state of the art cloud computing model building browser app.
Ajay- Describe your journey in science and technology from high school to your current job at Google.

Zach- First, thanks so much for the opportunity to do this interview Ajay!  My personal journey started in college where I worked at a startup named Invite Media.   From there I transferred to the Associate Product Manager (APM) program at Google.  The APM program is a two year rotational program.  I did my first year working in display advertising.  After that I rotated to work on the Prediction API.

Ajay- How does the Google Prediction API help an average business analytics customer who is already using enterprise software , servers to generate his business forecasts. How does Google Prediction API fit in or complement other APIs in the Google API suite.

Zach- The Google Prediction API is a cloud based machine learning API.  We offer the ability for anybody to sign up and within a few minutes have their data uploaded to the cloud, a model built and an API to make predictions from anywhere. Traditionally the task of implementing predictive analytics inside an application required a fair amount of domain knowledge; you had to know a fair bit about machine learning to make it work.  With the Google Prediction API you only need to know how to use an online REST API to get started.

You can learn more about how we help businesses by watching our video and going to our project website.

Ajay-  What are the additional use cases of Google Prediction API that you think traditional enterprise software in business analytics ignore, or are not so strong on.  What use cases would you suggest NOT using Google Prediction API for an enterprise.

Zach- We are living in a world that is changing rapidly thanks to technology.  Storing, accessing, and managing information is much easier and more affordable than it was even a few years ago.  That creates exciting opportunities for companies, and we hope the Prediction API will help them derive value from their data.

The Prediction API focuses on providing predictive solutions to two types of problems: regression and classification. Businesses facing problems where there is sufficient data to describe an underlying pattern in either of these two areas can expect to derive value from using the Prediction API.

Ajay- What are your separate incentives to teach about Google APIs  to academic or researchers in universities globally.

Zach- I’d refer you to our university relations page–

Google thrives on academic curiosity. While we do significant in-house research and engineering, we also maintain strong relations with leading academic institutions world-wide pursuing research in areas of common interest. As part of our mission to build the most advanced and usable methods for information access, we support university research, technological innovation and the teaching and learning experience through a variety of programs.

Ajay- What is the biggest challenge you face while communicating about Google Prediction API to traditional users of enterprise software.

Zach- Businesses often expect that implementing predictive analytics is going to be very expensive and require a lot of resources.  Many have already begun investing heavily in this area.  Quite often we’re faced with surprise, and even skepticism, when they see the simplicity of the Google Prediction API.  We work really hard to provide a very powerful solution and take care of the complexity of building high quality models behind the scenes so businesses can focus more on building their business and less on machine learning.

 

 

HANA Oncolyzer

An interesting use case of technology for better health is HANA Oncolyzer at http://epic.hpi.uni-potsdam.de/Home/HanaOncolyzer

“Build on the newest in-memory technology the HANA Oncolyzer is able to analyze even huge amounts of medical data in shortest time”, says Dr. Alexander Zeier, Deputy Chair of EPIC. Research institutes and university hospital support from HANA Oncolyzer by building the basis for a flexible exchange of information about efficiency of medicines and treatments.

In near future, the tumor’s DNA of all cancer patients needs to be analyzed to support specific patient therapies. These analyses result in medical data in amount of multiple terabytes. “These data need to be analyzed regarding mutations and anomalies in real-time”, says Matthias Steinbrecher at SAP’s Innovation Center in Potsdam. As one of the aims the research prototype HANA Oncolyzer was developed at our chair in cooperation with SAP’s Innovation Center in Potsdam. “The ‘heart’ of our development builds the in-memory technology that supports the parallel analysis of million of data within seconds in main memory”, saysMatthieu Schapranow, Ph.D. cand. at the HPI.

and

research activities result in 500.000 or more data points per patient.

and

With the help of a dedicated iPad application medical doctors can access all data mobile at any location anytime.

 

Research on Social Games

Social Gaming is slightly different from arcade gaming, and the heavy duty PSP3, XBox, Wii world of gaming.  Some observations on my research ( 😉 ) on social gaming across internet is as follows-

There are mostly 3 types of social games-

1) Quest- Build a town/area/farm to earn in game money or points

2) Fight- fight other people /players /pigs earn in game money or points

3) Puzzle- Stack up, make three of a kind, etc

Most successful social games are a crossover between the above three kinds of social games (so build and fight, or fight and puzzle etc)

In addition most social games have some in game incentives that are peculiar to social networks only. In game incentives are mostly in game cash to build, energy to fight others, or shortcuts in puzzle games. These social gaming incentives are-

1) Some incentive to log in daily/regularly/visit game site more often

2) Some incentive to invite other players on the social network

A characteristic of this domain is blatant me-too, copying and ripping creative ideas (but not the creative itself)  from other social games. In general the successful game which is the early leader gets most of the players but other game studios can and do build up substantial long tail network of players by copying games. Thus there are a huge variety of games.

However there are massive hits like Farmville and Angry Birds, that prove that a single social game well executed can be very valuable and profitable to both itself as well as the primary social network hosting it.

Accordingly the leading game studios are Zynga, Electronic Arts and (yes) Microsoft while Google has been mostly a investor in these.

A good website for studying data about social games is http://www.appdata.com/ while a sister website for studying developments is  http://www.insidesocialgames.com/

As you can see below Appdata is a formidable data gatherer here (though I find the top App – Static HTML as both puzzling and a sign of un corrected automated data gathering),

but I expect more competition in this very lucrative segment.

 

 

PiCloud gives away 20 free compute hours PER month

Announcement from PiCloud- (and this is apart from the 5 hours free that a beginner account gets)

http://www.picloud.com/

 

Starting this month, all users will get 20 c1 core hours worth of credits each and every month.

 

  • If you ran out of your original 5 core hour credits, you can come back and play around some more!
  • If you have minimal computing needs, this means that you can now use PiCloud regularly without even having to enter a credit card.

 

Looking for more? Don’t forget, we’re giving away $500 worth of credits as part of our Academic Research Program. Applications are due this Thursday, October 27th

Interview Markus Schmidberger ,Cloudnumbers.com

Here is an interview with Markus Schmidberger, Senior Community Manager for cloudnumbers.com. Cloudnumbers.com is the exciting new cloud startup for scientific computing. It basically enables transition to a R and other platforms in the cloud and makes it very easy and secure from the traditional desktop/server model of operation.

Ajay- Describe the startup story for setting up Cloudnumbers.com

Markus- In 2010 the company founders Erik Muttersbach (TU München), Markus Fensterer (TU München) and Moritz v. Petersdorff-Campen (WHU Vallendar) started with the development of the cloud computing environment. Continue reading “Interview Markus Schmidberger ,Cloudnumbers.com”