ACM Data Mining Camp
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:
- Patricia Hoffman, Ph.D, Machine Learning and Data Mining
The expert panel was represented by:
- Ted Dunning, Ph.D. Chief Technology Officer at DeepDyve
- Joseph B. Rickert Revolution Computing
- Giovanni Seni, Ph.D. Elder Research Inc. and Professor at Santa Clara University
- Michael Walker, Ph.D. Professor at Stanford University and President of Walker Bioscience
- Hugh Williams, Ph.D. Vice President, Search Engine Engineer, Buyer Experience Development eBay
- Mike Bowles, Ph.D. Seasoned in Startups, Data Mining and Quantitative Finance
- Greg Makowski Principal Consultant at Golden Data Mining
The Video
| ACM Data Mining Camp Expert Panel Discussion with Q&A March 20′th 2010, held at eBay San Jose CA |
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Products ReferencedUse these links to jump to publications discussed by the expert panel
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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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