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https://en.wikipedia.org/wiki/Machine_learning 
"Machine learningis the subfield of<A HREF="https://en.wikipedia.org/wiki/Computer_science" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">computer science</A>that "gives computers the ability to learn without being explicitly programmed" (<A HREF="https://en.wikipedia.org/wiki/Arthur_Samuel" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">Arthur Samuel</A>, 1959).<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-arthur_samuel_machine_learning_def-1" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[1]</A>Evolved from the study of<A HREF="https://en.wikipedia.org/wiki/Pattern_recognition" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">pattern recognition</A>and<A HREF="https://en.wikipedia.org/wiki/Computational_learning_theory" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">computational learning theory</A>in<A HREF="https://en.wikipedia.org/wiki/Artificial_intelligence" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">artificial intelligence</A>,<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-Britannica-2" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[2]</A>machine learning explores the study & construction of<A HREF="https://en.wikipedia.org/wiki/Algorithm" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">algorithms</A>that can<A HREF="https://en.wikipedia.org/wiki/Learning" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">learn</A>from & make predictions on<A HREF="https://en.wikipedia.org/wiki/Data" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">data</A><A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-3" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[3]</A> such algorithms overcome following strictly static<A HREF="https://en.wikipedia.org/wiki/Computer_program" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">program instructions</A>by making data driven predictions or decisions,<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-bishop-4" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[4]</A>:2through building a<A HREF="https://en.wikipedia.org/wiki/Mathematical_model" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">model</A>from sample inputs. Machine learning is employed in a range of computing tasks where designing & programming explicit<A HREF="https://en.wikipedia.org/wiki/Algorithm" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">algorithms</A>is infeasible; example applications include<A HREF="https://en.wikipedia.org/wiki/Spam_filter" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">spam filtering</A>, detection of network intruders or malicious insiders working towards a data breach,<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-5" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[5]</A><A HREF="https://en.wikipedia.org/wiki/Optical_character_recognition" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">optical character recognition</A>(OCR),<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-Wernick-Signal-Proc-July-2010-6" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[6]</A><A HREF="https://en.wikipedia.org/wiki/Learning_to_rank" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">search engines</A>and<A HREF="https://en.wikipedia.org/wiki/Computer_vision" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">computer vision</A>.
Machine learning is closely related to (and often overlaps with)<A HREF="https://en.wikipedia.org/wiki/Computational_statistics" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">computational statistics</A>, which also focuses in prediction-making through the use of computers. It has strong ties to<A HREF="https://en.wikipedia.org/wiki/Mathematical_optimization" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">mathematical optimization</A>, which delivers methods, theory & application domains to the field. Machine learning is sometimes<A HREF="https://en.wikipedia.org/wiki/Conflate" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">conflated</A>with<A HREF="https://en.wikipedia.org/wiki/Data_mining" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">data mining</A>,<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-7" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[7]</A>where the latter subfield focuses more on exploratory data analysis & is known as<A HREF="https://en.wikipedia.org/wiki/Unsupervised_learning" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">unsupervised learning</A>.<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-bishop-4" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[4]</A>:vii<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-8" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[8]</A>Machine learning can also be unsupervised<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-9" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[9]</A>and be used to learn & establish baseline behavioral profiles for various entities<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-10" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[10]</A>and then used to find meaningful anomalies.
Within the field of<A HREF="https://en.wikipedia.org/wiki/Data_analytics" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">data analytics</A>, machine learning is a method used to devise complex models & algorithms that lend themselves to prediction; in commercial use, this is known as<A HREF="https://en.wikipedia.org/wiki/Predictive_analytics" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">predictive analytics</A>. These analytical models allow researchers,<A HREF="https://en.wikipedia.org/wiki/Data_science" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">data scientists</A>, engineers, & analysts to "produce reliable, repeatable decisions & results" & uncover "hidden insights" through learning from historical relationships & trends in the data.<A HREF="https://en.wikipedia.org/wiki/Machine_learning#cite_note-11" TARGET="_blank" REL="noopener noreferrer noopener nofollow noopener noreferrer nofollow nofollow noreferrer nofollow">[11]</A>"
 - Wikipedia
 
 
 
Our distinguished panel will discuss the latest developments in machine learning. 
Please come with questions & comments.
 
If you are a machine learning startup & would like space on a demo table for the networking portion of the event, please send an email message to:
idea.to.ipo@gmail.com
 
Put this in the subject header:
Th 8/31 Machine Learning Startup Demo Table
 
Agenda
6:00 pm to 7:00 pm Check In, Food, Networking
7:00 pm to 8:30 pm Panel Discussion, Q & A
8:30 pm to 9:00 pm Informal Q & A, More Networking
 
About the Panelists: 
(More panelists TBA)
 
 
 
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Sophia Viklundis an entrepreneur, public speaker, advisor, mentor to startups, investor, & an advocate of women & girls in STEAM education (Science, Technology, Engineering, Art & Mathematics).
In addition to being an entrepreneur, Sophia is also an investor, & is a managing director at Golden Seeds, a network of women angle investors that invests in women-owned companies.
Sophia is currently centrally engaged as CEO of New Sun Technologies, a company that produces innovative products designed to connect, empower & excite massive user audiences around the world.Currently, she is working on an next-generation machine learning solution that uses behavioral analysis to monitor for early signs of important changes in physical or psychological health.
Sophia is a co-founder of Silicon Valley Deep Learning Group (SVDLG). SVDLG is a professional organization that was founded in 2015, at a time when Deep Learning was just beginning to emerge as one of the defining topics of the modern Silicon Valley Landscape. SVDLG currently has over 3,500 members who are engineers,scientists, developers, entrepreneurs & investors interested in artificial intelligence & deep learning, starting companies in deep learning & investing in the companies in that space.
Many times a successful founder, Sophias projects & companies are enhanced by her strong interest not only in technology, but education, design & experience of her customers. Prior to Silicon Valley Deep Learning Group, the previous company that Sophia co-founded specialized in serious games development, working with clients such as Gates Foundation, IARPA, & Lockheed Martin, among others, creating immersive educational games & training simulators for high-stakes environments.
Sophia was a co-founder of PyLadies, a world-wide educational initiative for women who program use Python, supported by Python Software Foundation & Google.
Sophia is a speaker for the US State Department on issues of women entrepreneurs & one of her public roles is to inspire more women around the world to start technology companies.
Twitter: @SophiaViklund
 
 
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Yaad Orenis vice president of of Platform & Technology Strategy at SAP, leading the Technology & Platform team in SAP Corporate Strategy. Yaad is working together with SAP leadership team to drive SAP Platform & Technology strategy, with a key focus on identifying new innovative technologies & growth opportunities. 
In his previous roles, Yaad was part of SAP Product & Innovation group for 10 years, filling several managerial positions in the areas of product management & development. 
 
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Preeti Rathiis a principal at Ignition Partners, an early stage business software venture capital firm based in Silicon Valley & Seattle. Preeti is passionate about working with & helping companies grow from seed or Series A startups to scalable businesses with significant traction. Her areas of interest include mobile enterprise apps, machine learning & software as a service(SaaS). 
Before joining Ignition, Preeti was at Opus Capital, where she invested in Turi (acquired by Apple) & Edunav. Prior to Opus, during her tenure at Juniper Ventures, Preeti invested in companies like Wickr (self-destructing messaging) & Vectra Networks (advanced threat detection).
Preeti has a strong operating background & has held various roles in product management & development in the areas of Enterprise Edge, SDN, & Mobility. Her experience includes defining product strategy & road maps for $1B revenue products like the ASR 9000 Series.
Preeti holds an M.S. in Computer Science from Stanford University & an MBA from The Wharton School of Business.
 
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Bryan Levensonis an angel Investor andan advisor, product / cloud strategist for several software companies.
Some of these companies includeCYTK, an innovator in Voice AI & Virtual Assistant technologies currently engaging large OEMs in the Automotive industry; Weather Analytics, a provider of predictive analytics & APIs enabling companies to source & incorporate reliable historical, current & forecast data worldwide; (Weather recently closed a $15 million financing round); & Pinn, a security company that leverages human biometrics to replace the common password. (Pinn has almost completed their 10 million Series A financing. )
During his career, Brian has held senior management positions at Viacom, IGN.com (which was eventually sold to Fox Entertainment for $640 million) & Intershop Communications AG which went public in Germany (Ish2:Gr).
Bryans business philosophy revolves around a human-centered, design-based approach to build products & services that uncover the latent needs, behaviors, & desires of people.
 
 
 
 
About the Moderator:
 
 
 
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Bill Ahmannis a partner in the Intellectual Property Practice Group in Sheppard Mullins Palo Alto office.
Bill focuses on intellectual property law in the fields of wireless networks, telecommunications, computers, software, semiconductors, electronics, security, business methods, & the Internet.
Other areas of his practice include patent prosecution, licensing, litigation, copyright, trademark procurement, & designs protection. Bill has worked with a number of clients, rendering patent opinions, including invalidity & noninfringement opinions, focusing on patent prosecution & patent analysis, & establishing internal patent procurement programs & competitive analysis.
Bill has drafted, supervised, and/or prosecuted approximately 600 patent applications, & has filed and/or prosecuted dozens of reexaminations. He has been involved in multiple litigations matters & monetization efforts. He also has served as primary counsel in many licensing negotiations.
Bill holds a J.D. from the University of California, Hastings College of Law & a B.S. in Computer Science from the University of Missouri.
 
 
 
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