Larry Page was Google’s founding CEO and grew the company to more than 200 employees and profitability before moving into his role as president of products in April 2001. He continues to share responsibility for Google’s day-to-day operations with Eric Schmidt and Sergey Brin.
The son of Michigan State University computer science professor Dr. Carl Victor Page, Larry’s love of computers began at age six. While following in his father’s footsteps in academics, he became an honors graduate from the University of Michigan, where he earned a bachelor’s degree in engineering, with a concentration on computer engineering. During his time in Ann Arbor, Larry built an inkjet printer out of Lego™ bricks.
While in the Ph.D. program in computer science at Stanford University, Larry met Sergey Brin, and together they developed and ran Google, which began operating in 1998. Larry went on leave from Stanford after earning his master’s degree.
In 2002, Larry was named a World Economic Forum Global Leader for Tomorrow. He is a member of the National Advisory Committee (NAC) of the University of Michigan College of Engineering, and together with co-founder Sergey Brin, Larry was honored with the Marconi Prize in 2004. He is a trustee on the board of the X PRIZE, and was elected to the National Academy of Engineering in 2004.
and no coincidence but it reminded me of the Metallica video- Turn the Page. Forgive the Pun, herr Eric
Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.
Ajay- Describe your career in science. How do you think can more young people be made interested in science.
Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).
I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.
That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.
Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book 😉
Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book
Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.
The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.
In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.
Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.
Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.
In other words: do not expect to become rich with the solution I describe in the chapter !
Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R
Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.
I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!
Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc
Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!
Ajay- What do you do to relax or unwind when not working?
Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.
Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.
For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-
For more information and to place an order, visit us at http://www.crcpress.com. Order online and apply 20% Off discount code 907HM at checkout. CRC is pleased to offer free standard shipping on all online orders!
Over the month long break I took, I was helping firm up my ideas for R for Analytics , I also took a break and read some books. Here are brief reviews of two, three of them-
1) Hindu Myths
This is a classical book translated from original Sanskrit written by Professor Wendy O Flaherty of University of Chicago. I found some of the older myths very interesting in terms of contradictions, retelling the same story in a modified way by another classic, the beautiful poetic and fantastic imagery evoked by Hindu myths. Some stories are as relevant in prayers, fasts and religious ceremonies as they were around 11000 years while most have morphed , edited or even distorted.
It should help the non Indian reader understand why hundreds of millions of conservative Indians worship Shiv Ling ( or literally an idol of the Phallus of Shiva), the Hindu two cents of creation of the universe, and the somewhat fantastic stories on super heroes /gods/ in the ancient world.
The book suffers from a few drawbacks in my opinion-
1) Sanskrit is a bit like Latin- you can lose not just the flavor but original meaning of words and situational context. Some of the stories made better sense when i read a more recent Hindi translation.
2) An excessive emphasis on sexual imagery rather than emotional imagery. The author seems wonder struck to read and translate ancient indians were so matter of fact about physical relationships. However the words were always written in discrete poetic than crass soft pornography.
3) Almost no drawings or figures. This makes the book a bit dense to read at 300 pages.
I liked another book on Hindu Myths (Myth= Mithya which I read in 2009) and you can see if you can read it if you find the topic interesting.
A Handbook of Hindu Mythology
Hindus have one God.
They also have 330 million gods: male gods, female gods, personal gods, family gods, household gods, village gods, gods of space and time, gods for specific castes and particular professions, gods who reside in trees, in animals, in minerals, in geometrical patterns and in man-made objects.
Then there are a whole host of demons.
But no Devil.
Mere Christianity by C S Lewis is a classic book on reinterpreting Christianity in modern times. However the author wrote this when World War 2 was on and it seems more like a British or Anglo Saxon interpretation of beliefs of Christ Jesus– who was actually a Jewish teacher born in Middle East Asia.
While the language and reading makes it much easier to read- it is recommended more at Western audiences, than Eastern ones, as it seems some of the parables are a more palatable re interpretation of the New Testament. The Bible is a deceptively easy book to read, the language is short and beautiful-and the original parables in the Gospels remain powerful easy to understand.
C S Lewis tends to emphasize morality than religiosity or faith, and there is not much comparison with any other faith or alternative morality. Dumbing down the Bible so as to market it better to reluctant consumers seems to be Mr Lewis intention and it is not as scholarly a work as an exercise in pure prose.
However it is quite good as a self improvement book and is quite better than the “You Can Win” kind of books or even business concept books.
Note- I find reading books on religion as good exercises in reading the fountain source of philosophies. As a polytheist- I tend to read more than one faith.
My annual traffic to this blog was almost 99,000 . Add in additional views on networking sites plus the 400 plus RSS readers- so I can say traffic was 1,20,000 for 2010. Nice. Thanks for reading and hope it was worth your time. (this is a long post and will take almost 440 secs to read but the summary is just given)
My intent is either to inform you, give something useful or atleast something interesting.
Sandro Saita from http://www.dataminingblog.com/ just named me for an award on his blog (but my surname is ohRi , Sandro left me without an R- What would I be without R :)) ).
Aw! I am touched. Google for “Data Mining Blog” and Sandro is the best that it is in data mining writing.
DMR People Award 2010
There are a lot of active people in the field of data mining. You can discuss with them on forums. You can read their blogs. You can also meet them in events such as PAW or KDD. Among the people I follow on a regular basis, I have elected:
He has been very active in 2010, especially on his blog . Good work Ajay and continue sharing your experience with us!”
What did I write in 2010- stuff.
What did you read on this blog- well thats the top posts list.
Still reading this post- gosh let me sell you some advertising. It is only $100 a month (yes its a recession)
Advertisers are treated on First in -Last out (FILO)
I have been told I am obsessed with SEO , but I dont care much for search engines apart from Google, and yes SEO is an interesting science (they should really re name it GEO or Google Engine Optimization)
Apparently Hadley Wickham and Donald Farmer are big keywords for me so I should be more respectful I guess.
Search Terms for 365 days ending 2010-12-31 (Summarized)
2009-12-31 to Today
test drive a chrome notebook
test drive a chrome notebook.
wps sas lawsuit
google maps jet ski
test drive chrome notebook
sas wps lawsuit
chrome notebook test drive
best statistics software
google maps jetski
donald farmer microsoft
best statistical software
What about outgoing links? Apparently I need to find a way to ask Google to pay me for the free advertising I gave their chrome notebook launch. But since their search engine and browser is free to me, guess we are even steven.
Clicks for 365 days ending 2010-12-31 (Summarized)
I love his political satire-sometimes not his politics- though he is a liberal (surprisingly most people from creative arts tend to be liberal- guess because they support and need welfare more, 🙂 ) Since I am in India- I call myself a conservative (when filing taxes) or liberal (when drinking er tea)
2) Hugh Mcleod- of Gaping Void is very different from Mike above, in the way an abstract painter would be from a classical
artist. I like his satire on internet, technology and personal favorite – social media consultants. Hugh casts a critical eye on the world of tech and is an immensely successful artist- probably the Andy Warhol of this genre in a generation.
3) Doug Savage of Savage Chickens http://www.savagechickens.com/ has a great series of funny cartoons based on chickens drawn on Post it notes. While his drawing is less abstract than Hugh’s above, he sometimes touches an irreverent note more like Hugh than anyone else.
5) Scott Adams of Dilbert http://www.dilbert.com/ is probably the first “non kid stuff” cartoonist I started reading-in fact I once wrote to him asking for advice on my poetry to his credit- he replied with a single ” Best of Luck email”
They named our email server in Lucknow, UP, India for him (in my business school at http://iiml.ac.in ) Probably the best of corporate toon humor. Maybe they should make the Dilbert movie yet.
Amazon just did a cluster Christmas present for us tech geek lizards- before Google could out doogle them with end of the Betas (cough- its on NDA)
Clusters used by Academic Departments now have a great chance to reduce cost without downsizing- but only if the CIO gets the email.
While Professor Goodnight of SAS / North Carolina University is still playing time sharing versus mind sharing games with analytical birdies – his 70 mill server farm set in Feb last is about to get ready
( I heard they got public subsidies for environment- but thats historic for SAS– taking public things private -right Prof as SAS itself began as a publicly funded project. and that was in the 1960s and they didnt even have no lobbyists as well. )
In realted R news, Dirk E has been thinking of a R HPC book without paying attention to Amazon but would now have to include Amazon
(he has been thinking of writing that book for 5 years, but hey he’s got a day job, consulting gigs with revo, photo ops at Google, a blog, packages to maintain without binaries, Dirk E we await thy book with bated holes.
Unique to Cluster Compute and Cluster GPU instances is the ability to group them into clusters of instances for use with HPC
applications. This is particularly valuable for those applications that rely on protocols like Message Passing Interface (MPI) for tightly coupled inter-node communication.
Cluster Compute and Cluster GPU instances function just like other Amazon EC2 instances but also offer the following features for optimal performance with HPC applications:
When run as a cluster of instances, they provide low latency, full bisection 10 Gbps bandwidth between instances. Cluster sizes up through and above 128 instances are supported.
Cluster Compute and Cluster GPU instances include the specific processor architecture in their definition to allow developers to tune their applications by compiling applications for that specific processor architecture in order to achieve optimal performance.
The Cluster Compute instance family currently contains a single instance type, the Cluster Compute Quadruple Extra Large with the following specifications:
23 GB of memory 33.5 EC2 Compute Units (2 x Intel XeonX5570, quad-core “Nehalem” architecture) 1690 GB of instance storage 64-bit platform I/O Performance: Very High (10 Gigabit Ethernet) API name: cc1.4xlarge
The Cluster GPU instance family currently contains a single instance type, the Cluster GPU Quadruple Extra Large with the following specifications:
22 GB of memory 33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture) 2 x NVIDIA Tesla “Fermi” M2050GPUs 1690 GB of instance storage 64-bit platform I/O Performance: Very High (10 Gigabit Ethernet) API name: cg1.4xlarge