Stanford Online Courses Delayed Indefinitely

Message from Stanford –

Dear Ajay Ohri,

We’re very excited for the forthcoming launch of Course Name. We’re sorry not to have gotten in touch lately – we’ve been busy generating lots of content, and the system is working really well. Unfortunately, there are still a few administrative i’s to dot and t’s to cross. We’re still hopeful that we’ll go live very soon – we hope not more than a few weeks late.

But since we don’t have a firm timeline right now, we’d rather leave this open and get back to you with a definitive date soon (rather than just promise you a date that’s far enough in the future that we can feel confident about it). We’ll let you know a firm date as soon as we possibly can.

We realize that some of you will have made plans around expecting the course to start in January, and we apologize for any difficulties that this delay may cause.

The good news is that the course is looking great, and we’re thrilled that over X,000 of you have signed up – we can’t wait for the course to start!

See you soon online!

Course Name Course Staff

Some interesting stats (and note the relative numbers)-

67,000 signups for Technology Entrepreneurship

58,000 signups for Cryptography

44,000 signups for Machine Learning

50,000 signups for Design and Analysis of Algorithms

Also see-

http://see.stanford.edu/

and

Check out these other courses:

Medicine

Civil Engineering

Electrical Engr.

Complex Systems

 

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 Rapid-I -Ingo Mierswa and Simon Fischer

Here is an interview with Dr Ingo Mierswa , CEO of Rapid -I and Dr Simon Fischer, Head R&D. Rapid-I makes the very popular software Rapid Miner – perhaps one of the earliest leading open source software in business analytics and business intelligence. It is quite easy to use, deploy and with it’s extensions and innovations (including compatibility with R )has continued to grow tremendously through the years.

In an extensive interview Ingo and Simon talk about algorithms marketplace, extensions , big data analytics, hadoop, mobile computing and use of the graphical user interface in analytics.

Special Thanks to Nadja from Rapid I communication team for helping coordinate this interview.( Statuary Blogging Disclosure- Rapid I is a marketing partner with Decisionstats as per the terms in https://decisionstats.com/privacy-3/)

Ajay- Describe your background in science. What are the key lessons that you have learnt while as scientific researcher and what advice would you give to new students today.

Ingo: My time as researcher really was a great experience which has influenced me a lot. I have worked at the AI lab of Prof. Dr. Katharina Morik, one of the persons who brought machine learning and data mining to Europe. Katharina always believed in what we are doing, encouraged us and gave us the space for trying out new things. Funnily enough, I never managed to use my own scientific results in any real-life project so far but I consider this as a quite common gap between science and the “real world”. At Rapid-I, however, we are still heavily connected to the scientific world and try to combine the best of both worlds: solving existing problems with leading-edge technologies.

Simon: In fact, during my academic career I have not worked in the field of data mining at all. I worked on a field some of my colleagues would probably even consider boring, and that is theoretical computer science. To be precise, my research was in the intersection of game theory and network theory. During that time, I have learnt a lot of exciting things, none of which had any business use. Still, I consider that a very valuable experience. When we at Rapid-I hire people coming to us right after graduating, I don’t care whether they know the latest technology with a fancy three-letter acronym – that will be forgotten more quickly than it came. What matters is the way you approach new problems and challenges. And that is also my recommendation to new students: work on whatever you like, as long as you are passionate about it and it brings you forward.

Ajay-  How is the Rapid Miner Extensions marketplace moving along. Do you think there is a scope for people to say create algorithms in a platform like R , and then offer that algorithm as an app for sale just like iTunes or Android apps.

 Simon: Well, of course it is not going to be exactly like iTunes or Android apps are, because of the more business-orientated character. But in fact there is a scope for that, yes. We have talked to several developers, e.g., at our user conference RCOMM, and several people would be interested in such an opportunity. Companies using data mining software need supported software packages, not just something they downloaded from some anonymous server, and that is only possible through a platform like the new Marketplace. Besides that, the marketplace will not only host commercial extensions. It is also meant to be a platform for all the developers that want to publish their extensions to a broader community and make them accessible in a comfortable way. Of course they could just place them on their personal Web pages, but who would find them there? From the Marketplace, they are installable with a single click.

Ingo: What I like most about the new Rapid-I Marketplace is the fact that people can now get something back for their efforts. Developing a new algorithm is a lot of work, in some cases even more that developing a nice app for your mobile phone. It is completely accepted that people buy apps from a store for a couple of Dollars and I foresee the same for sharing and selling algorithms instead of apps. Right now, people can already share algorithms and extensions for free, one of the next versions will also support selling of those contributions. Let’s see what’s happening next, maybe we will add the option to sell complete RapidMiner workflows or even some data pools…

Ajay- What are the recent features in Rapid Miner that support cloud computing, mobile computing and tablets. How do you think the landscape for Big Data (over 1 Tb ) is changing and how is Rapid Miner adapting to it.

Simon: These are areas we are very active in. For instance, we have an In-Database-Mining Extension that allows the user to run their modelling algorithms directly inside the database, without ever loading the data into memory. Using analytic databases like Vectorwise or Infobright, this technology can really boost performance. Our data mining server, RapidAnalytics, already offers functionality to send analysis processes into the cloud. In addition to that, we are currently preparing a research project dealing with data mining in the cloud. A second project is targeted towards the other aspect you mention: the use of mobile devices. This is certainly a growing market, of course not for designing and running analyses, but for inspecting reports and results. But even that is tricky: When you have a large screen you can display fancy and comprehensive interactive dashboards with drill downs and the like. On a mobile device, that does not work, so you must bring your reports and visualizations very much to the point. And this is precisely what data mining can do – and what is hard to do for classical BI.

Ingo: Then there is Radoop, which you may have heard of. It uses the Apache Hadoop framework for large-scale distributed computing to execute RapidMiner processes in the cloud. Radoop has been presented at this year’s RCOMM and people are really excited about the combination of RapidMiner with Hadoop and the scalability this brings.

 Ajay- Describe the Rapid Miner analytics certification program and what steps are you taking to partner with academic universities.

Ingo: The Rapid-I Certification Program was created to recognize professional users of RapidMiner or RapidAnalytics. The idea is that certified users have demonstrated a deep understanding of the data analysis software solutions provided by Rapid-I and how they are used in data analysis projects. Taking part in the Rapid-I Certification Program offers a lot of benefits for IT professionals as well as for employers: professionals can demonstrate their skills and employers can make sure that they hire qualified professionals. We started our certification program only about 6 months ago and until now about 100 professionals have been certified so far.

Simon: During our annual user conference, the RCOMM, we have plenty of opportunities to talk to people from academia. We’re also present at other conferences, e.g. at ECML/PKDD, and we are sponsoring data mining challenges and grants. We maintain strong ties with several universities all over Europe and the world, which is something that I would not want to miss. We are also cooperating with institutes like the ITB in Dublin during their training programmes, e.g. by giving lectures, etc. Also, we are leading or participating in several national or EU-funded research projects, so we are still close to academia. And we offer an academic discount on all our products 🙂

Ajay- Describe the global efforts in making Rapid Miner a truly international software including spread of developers, clients and employees.

Simon: Our clients already are very international. We have a partner network in America, Asia, and Australia, and, while I am responding to these questions, we have a training course in the US. Developers working on the core of RapidMiner and RapidAnalytics, however, are likely to stay in Germany for the foreseeable future. We need specialists for that, and it would be pointless to spread the development team over the globe. That is also owed to the agile philosophy that we are following.

Ingo: Simon is right, Rapid-I already is acting on an international level. Rapid-I now has more than 300 customers from 39 countries in the world which is a great result for a young company like ours. We are of course very strong in Germany and also the rest of Europe, but also concentrate on more countries by means of our very successful partner network. Rapid-I continues to build this partner network and to recruit dynamic and knowledgeable partners and in the future. However, extending and acting globally is definitely part of our strategic roadmap.

Biography

Dr. Ingo Mierswa is working as Chief Executive Officer (CEO) of Rapid-I. He has several years of experience in project management, human resources management, consulting, and leadership including eight years of coordinating and leading the multi-national RapidMiner developer team with about 30 developers and contributors world-wide. He wrote his Phd titled “Non-Convex and Multi-Objective Optimization for Numerical Feature Engineering and Data Mining” at the University of Dortmund under the supervision of Prof. Morik.

Dr. Simon Fischer is heading the research & development at Rapid-I. His interests include game theory and networks, the theory of evolutionary algorithms (e.g. on the Ising model), and theoretical and practical aspects of data mining. He wrote his PhD in Aachen where he worked in the project “Design and Analysis of Self-Regulating Protocols for Spectrum Assignment” within the excellence cluster UMIC. Before, he was working on the vtraffic project within the DFG Programme 1126 “Algorithms for large and complex networks”.

http://rapid-i.com/content/view/181/190/ tells you more on the various types of Rapid Miner licensing for enterprise, individual and developer versions.

(Note from Ajay- to receive an early edition invite to Radoop, click here http://radoop.eu/z1sxe)

 

Contribution to #Rstats by Revolution

I have been watching for Revolution Analytics product almost since the inception of the company. It has managed to sail over storms, naysayers and critics with simple and effective strategy of launching good software, making good partnerships and keeping up media visibility with white papers, joint webinars, blogs, conferences and events.

However this is a listing of all technical contributions made by Revolution Analytics products to the #rstats project.

1) Useful Packages mostly in parallel processing or more efficient computing like

 

2) RevoScaler package to beat R’s memory problem (this is probably the best in my opinion as it is yet to be replicated by the open source version and is a clear cut reason for going in for the paid version)

http://www.revolutionanalytics.com/products/enterprise-big-data.php

  • Efficient XDF File Format designed to efficiently handle huge data sets.
  • Data Step Functionality to quickly clean, transform, explore, and visualize huge data sets.
  • Data selection functionality to store huge data sets out of memory, and select subsets of rows and columns for in-memory operation with all R functions.
  • Visualize Large Data sets with line plots and histograms.
  • Built-in Statistical Algorithms for direct analysis of huge data sets:
    • Summary Statistics
    • Linear Regression
    • Logistic Regression
    • Crosstabulation
  • On-the-fly data transformations to include derived variables in models without writing new data files.
  • Extend Existing Analyses by writing user- defined R functions to “chunk” through huge data sets.
  • Direct import of fixed-format text data files and SAS data sets into .xdf format

 

3) RevoDeploy R for  API based R solution – I somehow think this feature will get more important as time goes on but it seems a lower visibility offering right now.

http://www.revolutionanalytics.com/products/enterprise-deployment.php

  • Collection of Web services implemented as a RESTful API.
  • JavaScript and Java client libraries, allowing users to easily build custom Web applications on top of R.
  • .NET Client library — includes a COM interoperability to call R from VBA
  • Management Console for securely administrating servers, scripts and users through HTTP and HTTPS.
  • XML and JSON format for data exchange.
  • Built-in security model for authenticated or anonymous invocation of R Scripts.
  • Repository for storing R objects and R Script execution artifacts.

 

4) Revolutions IDE (or Productivity Environment) for a faster coding environment than command line. The GUI by Revolution Analytics is in the works. – Having used this- only the Code Snippets function is a clear differentiator from newer IDE and GUI. The code snippets is awesome though and even someone who doesnt know much R can get analysis set up quite fast and accurately.

http://www.revolutionanalytics.com/products/enterprise-productivity.php

  • Full-featured Visual Debugger for debugging R scripts, with call stack window and step-in, step-over, and step-out capability.
  • Enhanced Script Editor with hover-over help, word completion, find-across-files capability, automatic syntax checking, bookmarks, and navigation buttons.
  • Run Selection, Run to Line and Run to Cursor evaluation
  • R Code Snippets to automatically generate fill-in-the-blank sections of R code with tooltip help.
  • Object Browser showing available data and function objects (including those in packages), with context menus for plotting and editing data.
  • Solution Explorer for organizing, viewing, adding, removing, rearranging, and sourcing R scripts.
  • Customizable Workspace with dockable, floating, and tabbed tool windows.
  • Version Control Plug-in available for the open source Subversion version control software.

 

Marketing contributions from Revolution Analytics-

1) Sponsoring R sessions and user meets

2) Evangelizing R at conferences  and partnering with corporate partners including JasperSoft, Microsoft , IBM and others at http://www.revolutionanalytics.com/partners/

3) Helping with online initiatives like http://www.inside-r.org/ (which is curiously dormant and now largely superseded by R-Bloggers.com) and the syntax highlighting tool at http://www.inside-r.org/pretty-r. In addition Revolution has been proactive in reaching out to the community

4) Helping pioneer blogging about R and Twitter Hash tag discussions , and contributing to Stack Overflow discussions. Within a short while, #rstats online community has overtaken a lot more established names- partly due to decentralized nature of its working.

 

Did I miss something out? yes , they share their code by GPL.

 

Let me know by feedback

Analytics 2011 Conference

From http://www.sas.com/events/analytics/us/

The Analytics 2011 Conference Series combines the power of SAS’s M2010 Data Mining Conference and F2010 Business Forecasting Conference into one conference covering the latest trends and techniques in the field of analytics. Analytics 2011 Conference Series brings the brightest minds in the field of analytics together with hundreds of analytics practitioners. Join us as these leading conferences change names and locations. At Analytics 2011, you’ll learn through a series of case studies, technical presentations and hands-on training. If you are in the field of analytics, this is one conference you can’t afford to miss.

Conference Details

October 24-25, 2011
Grande Lakes Resort
Orlando, FL

Analytics 2011 topic areas include:

Interview- Top Data Mining Blogger on Earth , Sandro Saitta

Surajustement Modèle 2
Image via Wikipedia

If you do a Google search for Data Mining Blog- for the past several years one Blog will come on top. data mining blog – Google Search http://bit.ly/kEdPlE

To honor 5 years of Sandro Saitta’s blog (yes thats 5 years!) , we cover an exclusive interview with him where he reveals his unique sauce for cool techie blogging.

Ajay- Describe your journey as a scientist and data miner, from early experiences, to schooling to your work/research/blogging.

Sandro- My first experience with data mining was my master project. I used decision tree to predict pollen concentration for the following week using input data such as wind, temperature and rain. The fact that an algorithm can make a computer learn from experience was really amazing to me. I found it so interesting that I started a PhD in data mining. This time, the field of application was civil engineering. Civil engineers put a lot of sensors on their structure in order to understand how they behave. With all these sensors they generate a lot of data. To interpret these data, I used data mining techniques such as feature selection and clustering. I started my blog, Data Mining Research, during my PhD, to share with other researchers.

I then started applying data mining in the stock market as my first job in industry. I realized the difference between image recognition, where 99% correct classification rate is state of the art, and stock market, where you’re happy with 55%. However, the company ambiance was not as good as I thought, so I moved to consulting. There, I applied data mining in behavioral targeting to increase click-through rates. When you compare the number of customers who click with the ones who don’t, then you really understand what class imbalance mean. A few months ago, I accepted a very good opportunity at SICPA. I’m looking forward to resolving new challenges there.

Ajay- Your blog is the top ranked blog for “data mining blog”. Could you share some tips on better blogging for analytics and technical people

Sandro- It’s always difficult to start a blog, since at the beginning you have no reader. Writing for nobody may seem stupid, but it is not. By writing my first posts during my PhD I was reorganizing my ideas. I was expressing concepts which were not always clear to me. I thus learned a lot and also improved my English level. Of course, it’s still not perfect, but I hope most people can understand me.

Next come the readers. A few dozen each week first. To increase this number, I then started to learn SEO (Search Engine Optimization) by reading books and blogs. I tested many techniques that increased Data Mining Research visibility in the blogosphere. I think SEO is interesting when you already have some content published (which means not at the very beginning of your blog). After a while, once your blog is nicely ranked, the main task is to work on the content of the blog. To be of interest, your content must be particular: original, informative or provocative for example. I also had the chance to have a good visibility thanks to well-known people in the field like Kevin Hillstrom, Gregory Piatetsky-Shapiro, Will Dwinnell / Dean Abbott, Vincent Granville, Matthew Hurst and many others.

Ajay- Whats your favorite statistical software and what are the various softwares that you have worked with.
Could you compare and contrast these software as well.

Sandro- My favorite software at this point is SAS. I worked with it for two years. Once you know the language, you can perform ETL and data mining so easily. It’s also very fast compared to others. There are a lot of tools for data mining, but I cannot think of a tool that is as powerful as SAS and, in the same time, has a high-level programming language behind it.

I also worked with R and Matlab. R is very nice since you have all the up-to-date data mining algorithms implemented. However, working in the memory is not always a good choice, especially for ETL. Matlab is an excellent tool for prototyping. It’s not so fast and certainly not done for ETL, but the price is low regarding all the possibilities for data mining. According to me, SAS is the best choice for ETL and a good choice for data mining. Of course, there is the price.

Ajay- What are your favorite techniques and training resources for learning basics of data mining to say statisticians or business management graduates.

Sandro- I’m the kind of guy who likes to read books. I read data mining books one after the other. The fact that the same concepts are explained differently (and by different people) helps a lot in learning a topic like data mining. Of course, nothing replaces experience in the field. You can read hundreds of books, you will still not be a good practitioner until you really apply data mining in specific fields. My second choice after books is blogs. By reading data mining blogs, you will really see the issues and challenges in the field. It’s still not experience, but we are closer. Finally, web resources and networks such as KDnuggets of course, but also AnalyticBridge and LinkedIn.

Ajay- Describe your hobbies and how they help you ,if at all in your professional life.

Sandro- One of my hobbies is reading. I read a lot of books about data mining, SEO, Google as well as Sci-Fi and Fantasy. I’m a big fan of Asimov by the way. My other hobby is playing tennis. I think I simply use my hobbies as a way to find equilibrium in my life. I always try to find the best balance between work, family, friends and sport.

Ajay- What are your plans for your website for 2011-2012.

Sandro- I will continue to publish guest posts and interviews. I think it is important to let other people express themselves about data mining topics. I will not write about my current applications due to the policies of my current employer. But don’t worry, I still have a lot to write, whether it is technical or not. I will also emphasis more on my experience with data mining, advices for data miners, tips and tricks, and of course book reviews!

Standard Disclosure of Blogging- Sandro awarded me the Peoples Choice award for his blog for 2010 and carried out my interview. There is a lot of love between our respective wordpress blogs, but to reassure our puritan American readers- it is platonic and intellectual.

About Sandro S-



Sandro Saitta is a Data Mining Research Engineer at SICPA Security Solutions. He is also a blogger at Data Mining Research (www.dataminingblog.com). His interests include data mining, machine learning, search engine optimization and website marketing.

You can contact Mr Saitta at his Twitter address- 

https://twitter.com/#!/dataminingblog