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ACM Data Mining Camp, Nov 13
ACM Data Mining Camp, November 13, 2010
Over 150 Participants and 6 Sponsors have already signed up, as of Oct. 23rd! Some participant companies represented include: eBay, Revolution Analytics, LinkedIn, Mathworks, Adchemy, Capital Ideas, Pacific Bioscience, Forbes, Cloudant, Microsoft, Lucid Imagination, Intel, Ask.com, I Cubed, Klout, Qualcomm, Salesforce.com Sony, IBM, Apple, Cisco, and Numenta. Stanford, UC Berkeley, UCSF, University of Victoria’s School of Health Informatics, and San Jose State will also be represented.
WHAT is an UNCONFERENCE or CAMP? An unconference is an event where users suggest topics, get together and discuss them in detail. This camp is focused on Data Mining, Analytics, Cloud Computing, Machine Learning, and the various applications of these technologies. There is an option to join the SF Bay ACM for $20 per year. Our last Data Mining Camp had 380 participants.
DISCUSSION FORUM:
SPONSORS:
- UCSC Extension in Silicon Valley (annual sponsor for all SF Bay ACM events!)
- eBay Thank you for once again providing the venue (11 rooms) + box lunches!
- LinkedIn, Revolution Computing, Association of Computing Machinery (ACM),Conrady Applied Science w/ BayesiaLab
LOCATION:
- eBay 2161 N First St, San Jose, CA 95131 (free parking)
- The auditorium seats 420 people and 10 other rooms are available for break out sessions
REGISTRATION:
To register for the camp please fill out the following REGISTRATION FORM
(Click on the link “Registration Form” above.)
The camp is free from 11:15 AM – 7:30 PM. Please register so we can provide enough food and prepare badges for you in advance.
If you plan on connecting a Mac to a projector, please bring your connector cables
SCHEDULE FOR THE DAY:
- 9:00 Pick up name tag, coffee, network
- 9:15 – 11:30 Training Class – Key Data Mining Algorithms Workshop by Dean Abbott (the cost of the training class is $30.00)
- FREE Data Mining Camp (11:15 – 7:30pm) (beverages & snacks included with RSVP)
- 11:15-Noon Arrive, register, network, brainstorm session topics
- Box lunches provided by eBay
- Noon Unconference welcome, 5 min / sponsor, hiring announcements
- 12:50 Expert Panel Questions and Answers
- 1:40 Audience members suggest a topic, get show of hands for interest, select session room size by interest level, select time slot for session. We recommend sessions have a) a leader, b) a blogger / note taker, and c) a timer so we can leave everybody 10 minutes to get to the next session. See the last SESSION MATRIX for example.
- 2:30 SESSION break out time slot 1
- 3:30 SESSION break out time slot 2
- 4:30 SESSION break out time slot 3
- 5:30 SESSION break out time slot 4
- 6:30 Share summary of sessions over pizza and salad (Thank you Sponsors!)
- Door prize drawings
- 7:30 Thank you and wrap up.
Click here to Join in on the discussion of session suggestions!
SCHEDULE DETAILS for Saturday, November 13th, 2010 (tentative)
TRAINING CLASS (9:00am):
9AM – 11:30AM Pay $30 for this class at Kagi. Click Here (If you plan on attending the Training Class 1) please pay now at Kagi 2) also register for the camp so we will have badges made for you)
Class Description: Data Miners are typically empirical modelers, striving for accurate models without having to force data to conform to assumptions. Additionally, most data mining software packages contain not just one technique, but several to dozens! However, it is difficult to build predictive and descriptive models without knowing what you have in your toolbox!
This workshop will describe key data mining algorithms commonly implemented in commercial and open source data mining software. The algorithms will be described in primarily qualitative terms, but will include how they are built and key “knobs” one uses to alter their behavior. Screen captures of options and results as found in several commercial and open source data mining packages will be shown throughout the workshop to show how these principles are implemented.
Dean Abbott Bio: Dean Abbott is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices.
Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including PAW, KDD, AAAI, IEEE and several data mining software users conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including SPSS-IBM Modeler (formerly Clementine), Unica PredictiveInsight (formerly Affinium Model), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands-on courses using Statistica (Statsoft), Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).
Here is the video of the Expert Panel from the last camp (March 20, 2010)
ACM Data Mining Camp: March 20, 2010 Expert Panel Discusion with Q&A
Moderator: Patricia Hoffman, Ph.D. Scientific Researcher
- Susan Holmes, PhD - Director of the Stanford Mathematical and Computational Sciences Interdisciplinary Program & Director of Statistic’s VIGRE program has been a Stanford professor since 1998 and is currently affiliated with the BioX program. Her research includes mathematical applications 1) in Biology – study of HIV evolution, phylogenetic trees, graphs and networks 2) Computational statistics, including nonparametric computer intensive methods such as the bootstrap. 3) Data Mining large messy biological datasets. With collaborators and students, she wrote open source R packages for analyzing trees (distory)and an interactive Java suite for identifying cells in microscope images (GemIdent). She is interested in Image analysis, Immunology, Cancer, HIV, and in teaching using simulations and web-based tools. She came to Stanford via at MIT, Harvard and Cornell
- Dean Abbott is President of Abbot Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices. He is teaching the morning class.
- Mike Bowles, Ph.D. Seasoned in Startups, Data Mining and Quantitative FinanceDr. Bowles was founder and Chairman of Board at iBeam Broadcasting, and founding CEO at Com21. He is experienced with quantitative finance and fully automated quantitative trading.
- Omid Madani Senior computer scientist at the Artificial Intelligence Center of SRI International. He interestes include in all aspects of intelligence and mind, as well as algorithms design and analysis. His current research revolves around the themes of large-scale learning and data mining, including learning in the presence of myriad concepts, online learning, and unsupervised learning, in particular exploring and engineering systems that learn their own many concepts (computational development). In the 2009 European PASCAL Challenge on Large-Scale Hierarchical Text Classification, with just over 12k classes, his team’s approach obtained top rankings from among 18 participants. He has successfully applied learning techniques to a number of information retrieval applications.
UP TO 40 DISCUSSION SESSIONS (2:30 to 7:20):
These topics are posted on the Discussion Forum:
- Session topics may include one or more focus such as…
- Experience level: beginners to experts
- Algorithms: Forecasting, clustering, text mining, sentiment, network analysis, collaborative filtering, fraud, machine learning, speech recognition, computer vision
- Verticals: Internet advertising, social networks, targeted marketing, financial services, medical, genetics, green tech, space science, mobile devices, startups
- Tools/Processes: Commercial, public domain, libraries, in SQL, in cloud, project or product management, SalesForce.com plug ins, CRM software plug ins
- User Groups: R, SAS, Salford Systems, Hadoop, Mahout, Matlab
- Help me: I am stuck on… I need guidance… How do you…? (but suggest topic of general interest)
- Participate in Our Data Mining Blog: Find birds of a feather, invite participants in a session, suggest or plan session ideas, update during the session, share in the summary of sessions or add a job posting
VIDEOGRAPHER / PHOTOGRAPHER:
- Ron Fredericks will be recording and web hosting sessions
- Blog Post for this event
- Examples: Webinars, Internet TV, event capture, web hosting, and DVDs
- Tag line: “Video production for exceptional people, brands, and products”
- Web site and video hosting: www.LectureMaker.com
- Phone: 408-390-1895
- Here is the link to my video blog post for your event:http://www.lecturemaker.com/2010/03/acm-data-mining-camp/
- coordinate recording your session – ronf@lecturemaker.com
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