LectureMaker, LLC » Mathematics http://www.lecturemaker.com Teach, Market, and Sell From a Broadcast Video Studio, Supported by Influential Speech Direction, and Distributed on Smart Internet Video Publishing Platforms Wed, 08 Feb 2012 02:06:40 +0000 en hourly 1 http://wordpress.org/?v=3.3.1 ACM Data Mining Camp 2010 – Expert Panel http://www.lecturemaker.com/2011/09/acm-data-mining-expert-panel/ http://www.lecturemaker.com/2011/09/acm-data-mining-expert-panel/#comments Tue, 20 Sep 2011 20:55:22 +0000 admin http://www.lecturemaker.com/?p=4899 Continue reading →]]>

Ron Fredericks writes: LectureMaker was on the scene again this year to record the annual event held at eBay Saturday November 13′Th, 2010. Watch the video and review some selected event photos – both are presented below on this page. Use the navigation table below the video to jump into the video or share selected highlights with others. Special thanks to eBay, ACM Data Mining Special Interest Group, and Austin Anderson of Spice Pictures, LLC. Austin supported me with his time, expertise, and professional video camera filming the day’s events.

The next ACM Data Mining Camp for 2011 will be held October 15′th at eBay: More Info

See all of LectureMaker’s video enhanced math publications here: www.lecturemaker.com/category/mathematics/

Expert Panel Discussion

The panel was just as lively as last year’s discussion. Follow the full video with all the Q/A to see what was interesting to the experts and students in attendance.

Moderator
Dr. Patricia Hoffman, Research Scientist

Panelists
and their signature question

Dr. Neel Sundaresan, Sr. Director and Head, eBay Research Labs at eBay
How does data mining apply to social and incentive networks?

Mr. Dean Abbott, Chief Scientist and Co-Founder at SmarterRemarketer, LLC
What tools are available to the data miner today?

Dr. Mike Bowles, Research Scientist and Start-up Executive
How has the field of stock market prediction changed over the past two years?

Dr. Hans Dolfing, Pattern Recognition Manager at Apple
What are the current challenges in Speech and handwriting recognition?

Dr. Susan Holmes, Stanford Professor, Statistics Department
What recommendations do you have for people starting out in data mining today?

Dr. Omid Madani, Senior Computer Scientist at SRI International
How has data mining changed as the scale of data has increased so dramatically in the last few years?

Dr. Lionel Jouffe, President/CEO at BAYESIA
Can you give us a brief example where a Bayesian network did a great job in diagnostics? [poor audio, the answer to this question not included in video]

The ACM Data Mining Camp Expert Panel Video

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This video is being hosted with LectureMaker’s Cross-domain Video Platform. It’s designed specifically for sharing lectures. Learn more about the real-time streaming, direct sharing of short video segments, and content metrics here

 

Video Topics and Navigation Table

Elapsed TimeDescription of Topics (plus hot links into the video)
0.0%Dr. Neel Sundaresan, Sr. Director and Head, eBay Research Labs at eBay
2.0%How does data mining apply to social and incentive networks?
12.0%Mr. Dean Abbott, Chief Scientist and Co-Founder at SmarterRemarketer, LLC
13.5%What tools are available to the data miner today?
15.7%Dr. Mike Bowles, Research Scientist and Start-up Executive
16.4%How has the field of stock market prediction changed over the past two years?
21.8%,Dr. Hans Dolfing, Pattern Recognition Manager at Apple
22.7%What are the current challenges in Speech and handwriting recognition?
26.8%Dr. Susan Holmes, Stanford Professor, Statistics Department
27.9%What recommendations do you have for people starting out in data mining today?
34.8%Dr. Omid Madani, Senior Computer Scientist at SRI International
35.6%How has data mining changed as the scale of data has increased so dramatically in the last few years?
43.3%Dr. Lionel Jouffe, President/CEO at BAYESIA
44.2%Audience Q/A
44.6%What is the next major unexplored frontier in data mining?
50.6%What are the difficulties in managing incentive networks?
55.2%Can you comment on using economic metrics in incentive networks?
60.2%How can you measure emotion or sentiment in text or through other means?
68.0%What tools are best for terabyte data mining?
72.4%What are the alternatives to data mining with terabytes?
79.3%Can you comment on how you approach a new data mining problem?
83.5%Is there a way to use incremental algorithms in data mining?
90.4%Can you comment on the effects of anonymizing data?
92.9%Can you comment on using domain knowledge in generating recommendations from large data sets?
99.5%Credits

This is what the share text above looks like…

ACM Data Mining Camp 2010 – Expert Panel
Moderated by Dr. Patricia Hoffman

Video Link

Photo Gallery

You can click on any thumbnail to start a slide show, then right click to enlarge selected photos captured during the event.

References

Dr. Patricia Hoffman Blog:
patriciahoffmanphd.com/

DJ Cline write-up, links, and photos:
www.djcline.com/2010/11/17/nov-13-2010-acm-data-mining-camp/

Revolution Analytics write-up:
blog.revolutionanalytics.com/2010/11/acm-data-mining-camp-1.html

Analytic Bridge write-up:
www.analyticbridge.com/group/conferences/forum/topics/acm-data-mining-camp-nov-13

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]]> http://www.lecturemaker.com/2011/09/acm-data-mining-expert-panel/feed/ 0 RHIPE: An Interface to Hadoop and R for Large and Complex Data Analysis http://www.lecturemaker.com/2011/02/rhipe/ http://www.lecturemaker.com/2011/02/rhipe/#comments Sun, 13 Feb 2011 20:17:06 +0000 admin http://www.lecturemaker.com/?p=3005 Dr. Saptarshi Guha's event video demonstrates his open-source interface between R and Hadoop called RHIPE to a packed Bay Area R User Group audience at Facebook. Continue reading →]]>


Ron Fredericks writes: Dr. Saptarshi Guha created an open-source interface between R and Hadoop called the R and Hadoop Integrated Processing Environment or RHIPE for short. LectureMaker was on the scene filming Saptarshi’s RHIPE presentation to the Bay Area’s useR Group, introduced by Michael E. Driscoll and hosted at Facebook’s Palo Alto office on March 9′th 2010. Special thanks to Jyotsna Paintal for helping me film the event.

Saptarshi received his Ph.D from Purdue University in 2010, having been advised by Dr. William S. Cleveland. Saptarshi works at Revolution Analytics in Palo Alto, as of the last update to this blog post.

Hadoop is an open source implementation of both the MapReduce programming model, and the underlying file system Google developed to support web scale data.

The MapReduce programming model was designed by Google to enable a clean abstraction between large scale data analysis tasks and the underlying systems challenges involved in ensuring reliable large-scale computation. By adhering to the MapReduce model, your data processing job can be easily parallelized and the programmer doesn’t have to think about the system level details of synchronization, concurrency, hardware failure, etc.

Reference: “5 common questions about Hadoop” a cloudera blog post May 2009 – by Christophe Bisciglia

RHIPE allows the R programmer to submit large datasets to Hadoop for a Map, Combine, Shuffle, and Reduce to process analytics at a high speed. See the figure below as an overview of the video’s key points and use cases.


The RHIPE Video

Note: On August 5′th 2011, LectureMaker’s new video player version 4.1 was put into use on this page… “Now you can hot link to video content, just like the Video Topics and Navigation Table below suggests.” — Ron Fredericks, Co-founder LectureMaker LLC.

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Video Topics and Navigation Table

Elapsed TimeDescription of Topics (plus hot links into the video)
0.00%Beginning
1.00%Introduction to Dr. Saptarshi Guha
3.00%Introduction to RHIPE
4.40%Analysis of very large data sets
8.10%Overview of Hadoop
13.6%High performance computing with existing R packages
15.6%,High performance computing with RHIPE: MapReduce interface to R
24.4%Case study: VOIP
30.3%Case study, step 1: Convert raw data to R dataset
35.4%Case study, step 2: Feed data to a reducer
41.6%Case study, step 3: Compute summaries
45.7%Case study, step 4: Create new objects
49.3%Case study, step 5: Statistical routines across subsets
52.7%Case study: VOIP summary
54.6%Another example: Dept. of Homeland Security
62.0%RHIPE on EC2: Indiana bio-terrorism project
72.8%Q: What is the discrepancy between sampled data?
78.7%RHIPE on EC2: Simulation timing
80.6%RHIPE Todo list
86.3%RHIPE Lessons learned
95.0%Q: What optimization methods were used?
99.9%Credits

 

Code Examples from the Video

Source code highlighter note: R and RHIPE language constructs are color coded and hot-linked to appropriate online resources. Click on these links to learn more about these programming features. I manage the R/RHIPE source code highlighter project on my engineering site here: R highlighter.

Move Raw Data Into Hadoop File System for Use In R Data Frames

Code (r)
  1. ## Case Study – VoIP
  2. ## Copy text data to HDFS
  3. rhput(‘/home/sguha/pres/voip/text/20040312-105951-0.iprtp.out‘,’/pres/voip/text‘)
  4. ## Use RHIPE to convert text data :
  5. ##   1079089238.075950 IP UDP 200 67.17.54.213 6086 67.17.50.213 15074 0
  6. ##   …
  7. ##   to R data frames
  8. input < – expression({
  9. ## create components (direction, id.ip,id.port) from Sys.getenv("mapred.input.file")
  10. v <- lapply(seq_along(map.values),function(r) {
  11. value0 <- strsplit(map.values[[r]]," +")[[1]]
  12. key <- paste(value0[id.ip[1]],value0[id.port[1]],value0[id.ip[2]]
  13.    ,value0[id.port[2]],direction,sep=".")
  14. rhcollect(key,value0[c(1,9)])
  15.  })})

 

Submit a MapReduce Job Then Retrieve semi-calls

Code (r)
  1. ## Case Study - VoIP
  2. ## We can run this from within R:
  3. mr< -rhmr(map=input,reduce=reduce, inout=c('text','map'), ifolder='/pres/voip/text', ofolder='/pres/voip/df',jobname='create'
  4.    ,mapred=list(mapred.reduce.tasks=5))
  5. mr <- rhex(mr)
  6. ## This takes 40 minutes for 70 gigabytes across 8 computers(72 cores).
  7. ##    Saved as 277K data frames(semi-calls) across 14 gigabytes.
  8.  
  9. ## We can retrieve semi-calls:
  10. rhgetkey(list('67.17.50.213.5002.67.17.50.6.5896.out'),paths='/pres/voip/df/p*')
  11. ## a list of lists(key,value pairs)

 

Compute Summaries With MapReduce

Code (r)
  1. ## Case Study - VoIP
  3. lapply(seq_along(map.values),function(i){
  4. ## make key from map.keys[[i]]
  5. value<-if(tmp[11]=="in")
  6. c(in.start=start,in.end=end,in.dur=dur,in.pkt=n.pkt)
  8. c(out.start=start,out.end=end,out.dur=dur,out.pkt=n.pkt)
  9. rhcollect(key,value)
  10. })
  11. })
  13. pre={
  14. mydata<-list()
  15. ifnull <- function(r,def=NA) if(!is.null(r)) r else NA
  16. },reduce={
  17. mydata<-append(mydata,reduce.values)
  18. },post={
  19. mydata<-unlist(mydata)
  20. in.start<-ifnull(mydata['in.start'])
  21. .....
  22. out.end<-ifnull( mydata['out.end'] )
  23. out.start<-ifnull(mydata['out.start'])
  24. value<-c(in.start,in.end,in.dur,in.pkt,out.start,out.end,out.dur,out.pkt)
  25. rhcollect(reduce.key,value)
  26. })

 

Compute Summaries With MapReduce Across HTTP and SSH Connections

Code (r)
  1. ## Example Compute total bytes, total packets across all HTTP and SSH connections.
  2. m < - expression({
  3. w <- lapply(map.values,function(r)
  4. if(any(r[1,c('sport','dport')] %in% c(22,80))) T else F)
  5. lapply(map.values[w],function(v){
  6. key <- if(22 %in% v[1,c('dport','sport')]) 22 else 80
  7. rhcollect(key, c(sum(v[,'datasize']),nrow(v)))
  8. })})
  9.  
  10. r < - expression(
  11. pre <- { sums <- 0 }
  12. reduce <-{
  13. v <- do.call("rbind",reduce.values)
  14. sums <- sums+apply(v,2,sum)
  15. },post={
  16. rhcollect(reduce.key,c(bytes=sums[1],pkts=sums[2]))
  17. })

 

Load RHIPE on an EC2 Cloud

Code (r)
  1. library(Rhipe)
  2. load("ccsim.Rdata")
  3. rhput("/root/ccsim.Rdata","/tmp/")
  4. setup < - expression({
  5. load("ccsim.Rdata")
  6. suppressMessages(library(survstl))
  7. suppressMessages(library(stl2))
  8. })
  9. chunk <- floor(length(simlist)/ 141)
  10. z <- rhlapply(a,cc_sim, setup=setup,N=chunk,shared="/tmp/ccsim.Rdata"
  11.    ,aggr=function(x) do.call("rbind",x),doLoc=TRUE)
  12. rhex(z)

 

References:

“I just watched the Saptarshi Guha video. It looks great!! Thank you! The picture is incredibly crisp, and the timeline tab is a nice touch for reviewing the film. Thank you!” -- Matt Bascom

VMware's open-source partnership with Cloudera offers you a virtual machine with Hadoop, PIG, and HIVE - download

The University of Purdue hosts the documentation and open-source code base for RHIPE - download

This is what the share text above looks like...

RHIPE: An Interface Between Hadoop and R
Presented by Saptarshi Guha

Video Link

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]]> http://www.lecturemaker.com/2011/02/rhipe/feed/ 6 Introduction to Math Blogging http://www.lecturemaker.com/2011/01/introduction-to-math-blogging/ http://www.lecturemaker.com/2011/01/introduction-to-math-blogging/#comments Fri, 21 Jan 2011 05:47:50 +0000 admin http://www.lecturemaker.com/?p=2967 This video demonstrates Ron's open-source math publishing software. Continue reading →]]>

Ron Fredericks writes: “Introduction to Math Blogging ” is a short overview of the WordPress Math Publisher plugin. Get an overview of the plugin from this video. Some topics include:

  • What is it
  • How to install it
  • How to use it, and
  • Support and the software development release cycle

To create short flexible videos like this one, go to LectureMaker’s design and purchase page

Get a copy of the plugin from the WordPress.org Plugin site: wordpress.org/extend/plugins/wpmathpub/

I include some comments on the WordPress developer ecosystem as a model to learn from and perhaps emulate:

  • Automation of the submission process for new plugins
  • Automation of the community metrics and satisfaction the use of individual plugins
  • Online engineering tools to help developers track milestones during the plugin’s lifecycle (ALM)
  • Use of forums for community support between developers and users of plugins

I encourage the viewer to consider that increased use of math is necessary in today’s competitive global economy. Using a blog with math publishing features is one way to support this effort.

Usage

As an example, when I embedded the wpmathpub [pmath size=xx] tag into my blog post — like this:

Code (mathtext)
  1. [pmath size=16](a^2+b^2)=a^2+2*a*b+b^2[/pmath]
  2.  
  3. Where:
  4. [pmath size=12]~a[/pmath] is defined as a
  5. [pmath size=12]~b[/pmath] is defined as b

I get the following mathematical equation displayed in my blog post:

(a^2+b^2)=a^2+2*a*b+b^2

where:
~a is defined as a
~b is defined as b

The video was designed, acted in, and edited by Ron Fredericks; while the video was produced and directed by Alex Gershowitz; in LectureMaker’s Sunnyvale Video Studio

I invite the viewer to pick up a copy of “Numerical Recipes” if they are using math equations in their blog posts:

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]]> http://www.lecturemaker.com/2011/01/introduction-to-math-blogging/feed/ 1 ACM Data Mining Camp 2009 http://www.lecturemaker.com/2010/03/acm-data-mining-camp/ http://www.lecturemaker.com/2010/03/acm-data-mining-camp/#comments Fri, 12 Mar 2010 01:08:06 +0000 ronf http://www.lecturemaker.com/?p=1216 Continue reading →]]>

Ron Fredericks writes: The Association of Computing Machinery (ACM) Machine Learning / Data Mining Camp event had a full house. The video presented here attempts to capture both the content and the excitement surrounding this event. Use the table presented below to learn more about the video’s content. Use your mouse to highlight topics presented by our moderator, Dr. Hoffman, and her panel of experts. Click on the dots, once the content has loaded, to jump directly to this content. Post comments related to this event or the online eMedia itself.

The event was moderated by:

The expert panel was represented by:

The Video

ACM Data Mining Camp Expert Panel Discussion with Q&A
March 20′th 2010, held at eBay San Jose CA
Get Adobe Flash player 10 If you prefer not to upgrade to flash 10, you may try playing the flash movie from this link: lmpremovie2.swf
Email this video Link this video Embed this video Video Player support page

Products Referenced

Use these links to jump to publications discussed by the expert panel

Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (Synthesis Lectures on Data Mining and Knowledge Discovery)

Giovanni Seni (Author), John Elder (Author), Robert Grossman (Series Editor)

Learning MySQL [Paperback]

Seyed M.M. (Saied) Tahaghoghi (Author), Hugh Williams (Author)

Web Database Applications with PHP & MySQL, 2nd Edition [Paperback]

Hugh E. Williams (Author), David Lane (Editor)

Navigating the Video’s Time Line

Have you noticed all those red naviagtion dots embedded into LectureMaker’s video player? These dots allow a viewer to quickly get a sense of what content is included in the video.

“I usually regret when people ask me to watch online video because I just don’t know if my time will be well spent – especially a long video – so I added these red dot hot navigation spots to put online video back on Internet time” – said Ron Fredericks, designer of LectureMaker’s video player.

With these navigation dots, the nature of “Watch my video” has really changed for the better!

Here is a list of navigation dots embedded into this video.  This may help you quickly find content of interest in this video.

Table 1: Navigation embedded into this video (red dots, a.k.a. bread crumbs)

Elapsed
Time		 Percent
Played		 Description
0 0% Introduce Dr. Patricia Hoffman, Applied Mathematics & Machine Learning
0:36 1.12% Introduce Dr. Ted Dunning, Chief Technology Officer at DeepDyve
1:08 2.00% TD: What makes a good data mining modeling opportunity (intro missed)?
2:50 5.23% Introduce Mr. Joseph B. Rickert\nRevolution Computing
3:16 6.02% JR: Can you compare and contrast data mining vs statistical analysis?
7:30 13.84% Introduce Dr. Giovanni Seni\nElder Research & Santa Clara University
8:01 14.65% GS: How do ensemble methods improve the accuracy of predictions?
10:16 18.92% Introduce Dr. Michael Walker\nStanford University & Walker Bioscience
11:09 20.40% MW: Machine learning & data mining in bio-tech & pharmaceuticals
12:66 23.78% Introduce Dr. Hugh Williams: eBay VP, Search Engine & Buyer Experience
13:37 25.08% RW: How does eBay search for items from the longtail?
16:40 30.71% Introduce Dr. Mike Bowles: Startups, Data Mining, & Quantitative Finance
16:58 31.23% MB: How do you use data mining for automated trading systems?
18:59 34.92% Introduce Mr. Greg Makowski\nPrincipal Consultant, Golden Data Mining
19:25 35.66% GM: How do you use SAS Enterprise Miner tool for fraud analysis?
20:40 38.04% MW: Has data mining been used to enhance personalized medicine?
23:11 42.70% TD: How do corporations take advantage of data mining?
25:30 46.98% MW: How can we use current tools to model biological systems?
27:32 50.73% MB: Can you elaborate on use of data mining tools to build trading models?
29:28 54.28% TD: What methods do you use to compare technical text data vs. other text?
32:45 60.33% GS: How can a data mining model be built to be more scalable and reusable?
36:45 67.81% JR/TD: What training can help statisticians move into data mining field?
41:18 76.17% TD: Is there value in mining household electricity and natural gas data?
44:36 82.14% GM/TD/GS: How would you approach a rare event target with data mining tools?
47:57 88.31% TD: Can Machine Leaning be augmented with text mining?
49:59 92.06% JR/MB: Do you see convergence between statistical analysis and data mining?
53:00 97.61% Audience Follow-up: household data collection of electricity and natural gas
54:09 99.63% Credits


Deep-Pixel Event Photograph

The seating area was completely full as attendees listened to the data mining expert panel.

The snapshot presented here shows LectureMaker’s high-definition portable broadcast video studio with two seperate video cameras and several other media sources. Two studio lights are seen on either side of the panel discussion table to help reduce video noise.

ACM Data Mining Camp Event held at eBay, San Jose – the snapshot

Event references

For details on the event, check on these two links:

SF Bay Area ACM Event web page

ACM Data Mining Camp Event LinkedIn web page

ACM Data Mining Camp Event Photos and Review by DJ Cline

If you are a presenter

There are several breakout sessions after the initial panel discussion. If you are a presenter for one of the breakout sessions please post a comment with your email address (not pubic) to this blog post so I can coordinate with you in advance

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]]> http://www.lecturemaker.com/2010/03/acm-data-mining-camp/feed/ 4 Bay Area R User Group 2009 Kickoff Video http://www.lecturemaker.com/2009/02/r-kickoff-video/ http://www.lecturemaker.com/2009/02/r-kickoff-video/#comments Wed, 25 Feb 2009 23:39:28 +0000 admin http://www.lecturemaker.com/2009/02/r-and-science-of-predictive-analytics-lecture-production/ Continue reading →]]>
Ron Fredericks writes: In February I attended the Bay Area R User Group meeting recently held at Predictive Analytics World 2009. Michael E. Driscoll, one of the two kickoff meeting co-chairs, was gracious enough to let LectureMaker capture the video for the event as a technical marketing “lighthouse” project.

Editor’s Note: New Bay Area useR Group Video can be found here:
RHIPE: An Interface Between Hadoop and R for Large and Complex Data Analysis

Today I am happy to present this video to my readers.

If you manage the marketing, feature roll-out, or web site design, for a social network or professional ecosystem, then you need the techniques presented in this video.

Watch this video to learn about:

  1. The open-source analytics programming language called R
  2. How Google and Facebook approach analytics to predict their web user community’s behavior
  3. Where to download R and get enterprise level support
  4. How the meeting co-chairs use R

The R and Science of Predictive Analytics: Four Case Studies in R – the Video

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Email this video Link to this video LectureMaker Video Player product and support page

Panel of four recognized R users from industry:

  • Bo Cowgill, Google
  • Itamar Rosenn, Facebook
  • David Smith, Revolution Computing
  • Jim Porzak, The Generations Network

Moderator and co-chair of Bay Area R User Group:

  • Michael E. Driscoll, Dataspora LLC



A live R demo
The co-chairs, Michael and Jim, presented a great overview of the R language. Here at LectureMaker, source code highlighting is supported with automatic links back to language documentation for 132 languages plus my own R highlighter.

For example, here is a short R program:

Code (r)
  1. x <- rnorm(100)            # 100 random numbers from a normal(0,1) distribution
  2. y <- exp(x) + rnorm(100)   # an exponential function with error
  3. result <- lm(y ~ x)        # regress x on y and store the results
  4. summary(result)            # print the regression results
  5. plot(x,y)                  # pretty obvious what this does
  6. abline(result)             # add the regression line to the plot
  7. lines(lowess(x,y), col=2)  # add a nonparametric regression line (a smoother)
  8. hist(result$residuals)     # histogram of the residuals from the regression
  9. # source code reference: http://bayes.math.montana.edu/Rweb/Rweb.general.html

You can click on the highlighted methods, or copy the code above and paste it into a web version of R called RWeb

How I developed the video
The LectureMaker video capture, editing, publishing, and technical marketing service starts with the on location video capture of a live event. Druing the process I become emersed in the presenter(s) and the audience participation. My empathy acquired during the project combined with my own professional background allows me to turn video into technical marketing.

I watched the video presented here myself several times: Starting with the content through the lens of my Canon XL-H1S 3CCD HDV High Definition Professional Camcorder with 20x HD Video Zoom Lens III
prosumer digital video camera during the event, followed by several editing iterations on my eVGA e-GeForce GTX280 1GB DDR3 PCI-Express 2.0 Graphics Card-Lifetime Warranty with Free Special Edition EVGA Precision Overclocking Utility
graphics powered PC, ending with comprehensive testing from several Linux web servers connected to Mac, Linux, and Windows client PCs.

To create the video I took the following steps to edit and publish the video, primarily with Adobe Creative Suite 4 Master Collection
software:

  • Edit the video with Adobe After Effects and Premier Pro CS4. I incorporated the features of the Neat Video Pro noise reduction plug-in to correct for low light high gain raw camera issues
  • Edit and optimize the sound track separately using Adobe Soundbooth CS4 along with brightening of the speech itself as only Toastmasters International experience can provide
  • Design and develop a state of the art Video Player with custom hot spot navigation dots, intelligent preloader, and client-server progressive content management using Adobe Flash Professional CS4 ActionScript 3.0
  • Customize the LectureMaker Video Player with external preloader graphic image, personalized playback faceplate, selection of a playback control skin, and custom buttons using Adobe Illustrator and Photoshop CS4 Extended
  • Compress the 75 minute 200 GB high definition 1440 x 1080 mpeg video down to a 85 MB 800 x 600 flash video using both Adobe Media Encoder CS4 and Sorenson Squeeze 5 Pro
  • Lastly, I hosted the video inside this WordPress.org blog package currently at version 2.71 along with KFE SWFObject flash movie publisher plug-in

    • Some thoughts for next time: I did not have ideal placement for the video camera so I struggled with noise, light, who is talking when, and tripod stability. I made a few mistakes with sound and white balance during live experiments with the 100 or so buttons on this new camera too. I have since solved these issues with more experience, an after-market Sony XEL-1 11-Inch OLED Digital TV
    and wired remote control. I apologize to you the viewer, the R presenters, and attendees who participated with their questions. Hopefully these errors will seem small thanks to the excellent delivery of great material during the R User Group event itself. This was one of the best panel discussions I have attended in either engineering or science.

Contact LectureMaker to develop a video enhanced technical marketing program
This blog post’s content and its longtail of moderated comments over time demonstrates LectureMaker’s live video capture, video editing, social networking outreach, and technical marketing service. It might be based on a single live event, or a series of events integrated into a year-long community outreach program. What’s more, most of the cost for the LectureMaker service is already built into your current editorial events calender! Yet adding LectureMaker service to your existing events program could make all the difference in meeting your goals around ecosystem or sales development. You can get a brief overview here:
http://www.lecturemaker.com/products_services/

LectureMaker targets this service to exceptional people, brands, and products. Turn your high value live but localized events into reusable global marketing and education programs

How the panelists use R for predictive analytics
Each panelist came prepared to discuss R’s strengths and weaknesses as a tool, along with example case studies. Mike has a great blog post summarizing this meeting. So I invite those who would like to learn more about the techniques presented in this video to jump over to read it now:

How Google and Facebook are using R
by Michael E. Driscoll | February 19, 2009
http://dataspora.com/blog/predictive-analytics-using-r/

At the end of this process I learned a great deal about the material presented: including R packages, where to download it, who to contract as project mentors, who to lead group training, and some additional open-source packages such as Octave and R’s interface with Python. I Developed a passion for the techniques discussed in this video, and every video product I put together. But in this case I also plan to get started with R along with some of the integrated tools that were discussed in the near future.

Want to learn R? Here is a link to the book recommended by the R User Group:
Software for Data Analysis: Programming with R (Statistics and Computing)

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