(https://aiworld.com)
Seminar: Machine Learning & Deep Learning in the Enterprise Monday, December 11, 2017 - 1:00-4:45pm
STRATEGIC DEEP LEARNING PARTNER
(https://deepsense.ai/)
Seminar Introduction This half day seminar provides a comprehensive introduction to attendees on the entire machine learning (ML) and deep learning (DL) industry, including the different players, options, and technologies. A particular focus will be on DL to enable attendees to gain the basic concept of deep learning, which revolutionizes data science tasks such as image recognition, speech analysis or time series prediction. Participants will:
SPEAKERS
gain a thorough overview of today’s machine learning and deep learning market, learn when to use machine learning and when to use deep learning, gain knowledge about how and where to get and use the right type of data for different applications, learn where in your organization you can find the best type of applications, (https://aiworld.com/speakers/janlasek/) Jan Lasek
Senior Data Scientist, deepsense.ai
learn what type of approach your company should use in terms of building your own team and/or working with third party service and software providers, and what options are now available in the cloud. Attendees will also gain a deep sense of understanding of what steps to take next and will be well equipped to begin individual exploration.
1:00pm State-of-the-Practice of Data Science in the Enterprise Speaker: Jan Lasek, Senior Data Scientist, deepsense.ai (https://aiworld.com/speakers/robertbogucki/) Robert Bogucki
Chief Science Officer, deepsense.ai
The first part of this seminar will focus on providing a comprehensive introduction to attendees on the entire machine learning (ML) and deep learning (DL) industry, including different options, players, and technologies. This session will present an overview of the data science solutions and ecosystem, with an emphasis on actual examples of large corporate machine/deep learning deployments. Attendees will learn about the current ML and DL market and will hear about innovative uses of data science in different industries. Attendee benefits include:
(https://aiworld.com/speakers/alessandrozolla/) Alessandro Zolla
VP Technology, Machine Learning Program Lead, Nielsen
Learn about landscape and ecosystem of today’s data science market – the most popular and effective trends Get an overview of the options that enterprises have in developing a business and technology strategy to help them determine the best way to take advantage of data science today Numerous examples of data science solutions in different industries including FinTech, Healthcare, Consumer Service and others This seminar is geared to technical business management level professionals, however deep understanding of computer science, programming or data science is not required This portion of the seminar will answer:
(https://aiworld.com/speakers/vishwakolla/) Vishwa Kolla
Head of Advanced Analytics John Hancock Insurance
How to unlock the power of data science? What kind of data is used for data science purposes and what type of data should not be used? What is the timeframe for data science projects to pay off? How to do a proof of concept project? How long will your initial deployment be? What is the best software to use? What are the maintenance requirements? Q&A session
(https://aiworld.com/speakers/scottclark/) Scott Clark CEO, SIGOPT
2:00pm The What, Why and How of Deep Learning Speaker: Jan Lasek, Senior Data Scientist, deepsense.ai This session focuses on how to determine which industry’s and types of applications are best suitable to use deep learning. The presentation will be geared to business and technical managers and will describe use cases and statistics from different industries. Topics covered include: what deep learning is (popular models and techniques) and how it is different than machine learning
(https://aiworld.com/speakers/jakubczakon/) Jakub Czakon
Senior Data Scientist, deepsense.ai
what specific benefits can a business derive from using deep learning what are the current automation solutions, new market and data driven solutions, deep learning enterprise case studies and statistics will be discussed across multiple industries how businesses can begin implementing deep learning today to develop competitive advantage, save money and drive better customer experience. deployment timeframes what is the best software/platform to use based on different scenarios and applications what are the maintenance requirements, Q&A session
(https://aiworld.com/speakers/anandoka/) Anand Oka
Principal Program Manager, Anti-Fraud, Microsoft
2:45-3:15 Networking break on exhibit floor
3:15PM Panel: Deep Learning in the Enterprise – Opportunities and Challenges Presenter: Robert Bogucki, Chief Science Officer, deepsense.ai (https://aiworld.com/speakers/mikemacintyre/) Mike Macintyre
Chief Data Scientist, Panaseer
CONFERENCE AGENDA
Panelists: – Alessandro Zolla, VP Technology – Machine Learning Program Lead, Nielsen – Vishwa Kolla, AVP and Head of Advanced Analytics, John Hancock Insurance – Scott Clark, CEO, SIGOPT – Jakub Czakon, Senior Data Scientist, deepsense.ai This panel session will take attendees to the next step from the earlier strategic overview of the DL market. Panelists will include experienced deepsense.ai project managers who have run ML engagements and professionals from large enterprises who have deployed DL applications at their company. During this panel, you will hear from world class heads of data science and innovation departments for major U.S. companies. Learn what they are doing at their companies, how DL is disrupting their industries, and critical lessons learned in deploying enterprise class machine learning applications. Attendees will learn about: – The current state of the market in terms of need for machine learning solutions. – Vision, manufacturing, bots – what seems to be most promising use of ML, now? – The landscape of today’s machine language vendors and consultants, and how to pick and choose partners for success – What are the limitations of DL – What costs and ROI should be expected for various types of ML deployments? – Case studies describing what the costs, ROI and rewards have been from early adopters of DL. – The enterprise and organizational challenges in bringing DL in-house – How to construct an effective plan for a successful DL deployment project(s) – How to deal with upper management expectations – In-house vs. Outsource considerations: When do you want to build your internal machine learning team and when is more beneficial to work with external experts? – What are new business sectors where DL solutions are not widely adopted yet, but which have potential? – What would be the sign that your business needs machine learning? – What would you recognize as the most promising trends and technologies for a future? – Q&A session
4:00pm Panel: AI, Deep Learning and Cybersecurity Moderator: Jakub Czakon, Senior Data Scientist, deepsense.ai Panelists: Anand Oka, Principle Program Manager, Anti-Fraud, Microsoft Mike MacIntyre, Chief Scientist, Panaseer This panel will discuss the latest developments in machine learning and deep learning based fraud and abuse protection as a critical component of running a successful and socially responsible business. Applications: We will talk about three major application of AI in fraud protection, namely Purchase Fraud (e.g. stolen credit cards), Credit fraud (e.g. unpaid invoices) and abuse of services and facilities post-purchase (e.g. spam). We will discuss the key KPIs in each of these applications, and how the impact of ML/AI is assessed. Semi-Supervised learning: We will discuss how we used semi-supervised learning to best leverage the special domain knowledge that fraud analysts possess in each of our lines of business, while still getting the efficiencies inherent in ML automation. Privacy and explain-ability: Finally, we will also touch upon the implications of the European Union’s General Data Protection Regulation (GDPR) for machine learning, and how we stay compliant with the law by using techniques such as encrypted features, isolation of data and models, and explain-ability of the fraud scores that the models produce.
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