Interview John Myles White , Machine Learning for Hackers

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
About-
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

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You can read more in his own words at his blog at http://www.johnmyleswhite.com/about/
He can be contacted via social media at Google Plus at https://plus.google.com/109658960610931658914 or twitter at twitter.com/johnmyleswhite/

Data Mining Music

AA classic paper by Donald E Knuth (creator  of Tex) on the information complexity of songs can help listeners of music with an interest in analytics. This paper is a classic and dates from 1985 but is pertinent even today.

 

Movie Review- Bollywood "Rock Star"

This one is a wee bit different. The music sounds more contemporary and fusion ,East blends West, the direction is both subtle and at times flamboyant, and the acting is notches above the average dance round the tree, laugh like a idiot fare.

Rock Star is the tale of a college dropout (aint they all!) called Jordan / by Ranbir Kapoor  musical prodigy who battles personal demons, lady luck , lady love (in the impossible pout of the American fashion model ,Nargis F), and his own musical ambitions. The breathtaking art moves from Kashmir, Prague, Europe and the streets of Delhi University. This one is a taker, breaker, soul shaker.  Try getting a sub titled DVD if you dont know Hindi, or atleast dekko some songs streaming free at http://www.saavn.com/search/hindi/album%20Rockstar

Imitiaz Ali http://en.wikipedia.org/wiki/Imtiaz_Ali_(director) does all olde Hindu College alumni proud (including this reviewer)
https://www.youtube-nocookie.com/v/cn1jx_JUpi0?version=3&hl=en_US&rel=0

Movie Review – Sahib Biwi aur Gangster (The Lord, The Wife and the Gangster)

This is the latest Bollywood smash, it is a inspired version of Sahib < Biwi < and Ghulam

It features a feudal lord, doing what gentlemen borne as gentlemen do, his bored schizophreniac wife, and the driver/gangster

There are lots of twists and turns, and it may be Bollywood’s steamiest romantic comedy/drama.

The songs are nice, the acting is superb by the cast of Randeep Hooda (D, Once upon a time in Mumbai), Mahi Gill and Jimmy Shergill.

Power packed and nice- but see it without your kids/family or you may end up blushing! Probably the closest to a noir sex comedy that I have seen in 3 decades of Bollywood watching!

 

Ah! The Internet.

On the Internet I am not brown or black or white. I am Anonymous and yet myself. I am free to choose  whatever identity I wish to choose, free to drink from whatever pools of knowledge my local government wishes to forbid. The Internet does not care about how rich or poor I may be. It has ways to track exactly where I am, but it has tools to disguise that as well. On the internet the strongest government, the richest corporation and the deepest pockets can tremble before the bits and bytes of a talented and motivated hacker working from his basement in his parents house.
There are no losers on the Internet: only winners. Except for those who seek to covet and control the uncontrollable- the human desire to seek knowledge beyond the confines of whatever cave they may find themselves borne in.
There are no countries to wage war on the Internet: there is nothing to kill and die for. The Internet allowed a million writers to write and publish without the interference of brokers and intermediaries. It allowed a billion people to download a trillion songs that were locked away in some rich man’s virtual vault. It allowed a dozen countries to overthrow their dictators without wasting a billion worth of goods and treasure.

On the Internet, everyone is equal, free and true to the own nature they choose, not the fate that is chosen by corporation, country or circumstance.
Ah! The Internet- it will set you free.

Love Bytes- a modern Indian play

Review= Love Bytes

I was here in Taj Vivanta,Mumbai India, as a guest and I caught up with a lovely new age play called Love Bytes.

Directed and written by Divya Palat http://www.divyapalat.com  , this play explores the emotional turbulences of the digiterati, the Facebook status changes, and the complete life cycle of the supply chain of love.

From teenage puppy love, to double income 1 kid, to geeky nerd afraid to talk to the beauteous girl in office,and the live in couple with no pressure to marry – this s when Harry met Sally  plus Its Complicated all mashed together with anthemic rock songs between the acts.

I loved the humor and the satire though the second act did drag on my nerves with the puns on African American rappers,  extinct parsis  species, and mild homophobia in father son relationships. One solo act stood out for the funny words..

Welcome brave new world of Bollywood.

Or maybe I should say Bollyway-wood.