Program | Madrid Machine Learning - Machine Learning Conference | 2ML

PROGRAM

Luis Martín (Barrabés.biz)

Opening Remarks

15'

Luis Martín (Barrabés.biz)

State Of The Art In Machine Learning

Francisco Martín (BigML)

Machine Learning: A Technical Perspective

In this talk, Dr. Francisco Martín will talk about the state of the art in Machine Learning from a technical perspective and the impact ML is having on our society. We will learn about the ML evolution, where we are, and where we are going, as well as the advantages that ML is bringing, such as the automation of tasks and processes that until now only highly educated professionals were able to perform. All this will be explained from a realistic perspective.

30'

Francisco Martín (BigML)

Ed Fernández (Naiss.io)

Machine Learning: A Business Perspective

Machine Learning is touted the ultimate driver of digital transformation, albeit very much overhyped (according to Gartner) as this technology reaches beyond the peak of inflated expectations this year. In this talk we will review the state of Machine Learning from a business perspective, the Venture Capital and M&A trends in ML & AI, the enterprise AI scene, ML market adoption trends, and the evolution and platformization of Machine Learning in the Enterprise

30'

Ed Fernández (Naiss.io)

Real-World Use Cases

ML for Entertainment & Sports

Poul Petersen (BigML)

Predicting the 2018 Oscar Winners with Machine Learning

Is it possible to predict the Oscars? BigML’s Deepnets predicted 6 out of 6 Oscar categories right: best picture, best director, best actress, best actor, best supporting actress, and best supporting actor. But how is it possible to predict a seemingly random event? In this talk, M.Sc. Poul Petersen, Chief Infrastructure Officer at BigML will show how to approach a problem like predicting the Oscars, how to choose the data that is relevant, how to prepare the data, and how BigML's Deepnets work behind the scenes to give the best possible model for your machine learning problem.

30'

Poul Petersen (BigML)

José Ángel Alonso Cuerdo (KPMG)

Game Scheduling System

Accurately designing the "best" sports calendar is often difficult, if not impossible. Stakeholders often have conflicting objectives and constraints, and an optimal timetable is one that dissatisfies stakeholders as little as possible: federations, leagues, clubs, televisions, players, etc. At KPMG we have designed calendars using Advanced Analytics for major sports leagues worldwide. Among others, the NBA, Major League Baseball, the Australian Football League, the Big XII Athletics Conference, the South Eastern Athletic Conference and the Atlantic Coast Conference. We believe that there is no need to use a massive IT infrastructure to solve the problem of optimizing sports calendars. Our flexible optimization solutions incorporate ML models that allow you to optimize decision making to solve the most critical challenges in the search for the optimal calendar as quickly as possible.

30'

José Ángel Alonso Cuerdo (KPMG)

Coffee Break and Networking

30'

ML for Law & Industry

Arnoud Engelfriet (JuriBlox B.V.)

How to Make a Lawyerbot that Can Review NDA’s

The non-disclosure agreement (NDA) or confidentiality agreement is a staple of the business community, in particular the high-tech and IT business. It has been suggested the NDA is the most-used legal document in business. Due to their ubiquitous nature and routine use, NDA’s are generally regarded as a standard document. From a legal perspective, one couldn’t be more wrong. However, there is something of a gap in the market between what lawyers need to review an NDA and what businesspeople are willing to pay.

30'

Arnoud Engelfriet (JuriBlox B.V.)

Antonio Gracia Berná (Boeing)

The Aircraft Genome Project

30'

Antonio Gracia Berná (Boeing)

Jordi Palau (Celsa) & Joel Montoy (Aquiles Solutions)

AI Applied to Optimize the End-to-End Supply Chain in Celsa Group

For the last 3 years Celsa Group together with Aquiles Solutions have been developing software to optimize all the steps of the process of planning, sourcing, manufacturing and delivering steel (End-to-End Supply Chain). We will explain the different solutions that we have developed to: (1)Plan sourcing of raw materials (Rolls) (2) Optimize melting and casting processes to decrease beam blank / billet defects. (3)Optimize beam blank / billet length to decrease yield loss and improve profitability. (4) Schedule a multi-format Melting Shop. (5)Schedule rolling mills. (6)Optimize the use of stock and improve customer service through an ATP (available to promise software). (7)Automate the introduction of orders through a software that reads and translates directly from customer’s e-mails. (8)Calculate price quotations for complex orders of bespoken products (Reinforcing steel)

30'

Jordi Palau (Celsa) & Joel Montoy (Aquiles Solutions)

Panel of Experts

30'

Lunch Break and Networking

75'

ML for Social Good in Emerging Markets

David del Ser (BFA Global)

ML 4 Good: Robots for a Better World

Machine Learning is causing deep changes in rich economies as it disrupts industry after industry. But what is the story in emerging markets, where many poor consumers can't even access basic services? This talk will review a number of leading examples of innovators using ML to make financial services more accessible, affordable and appropriate for low-income consumers in Africa, Latin America and Asia.

30'

David del Ser (BFA Global)

Thor Muller (Off Grid Electric)

Managing High Risk Customer Credit with Machine Learning

Billions of people in emerging economies are unbanked with no credit history, yet they have growing incomes and a desire for modern products and services. Until recently, the risk involved with offering products on credit was too risky for most businesses, which has amplified the underserved nature of these markets. But things are changing: behavioral and profile data combined with Machine Learning are giving us the insight we need to target the customers best able to pay, as well as support optimal payment behavior over time.

30'

Thor Muller (Off Grid Electric)

Guillermo Caudevilla (Frogtek)

Empowering Mexican Microretailers Using Data and Machine Learning

In this talk we will learn how Frogtek helps base-of-the-pyramid Mexican micro-retailers to better control their businesses. Using its point of sale system they can register every transaction that takes place in the shop getting in exchange access to metrics and value added services fueled by their own and other shopkeepers data. Aggregate data feed a business intelligence and marketing analytics system that Consumer Packaged Goods companies use to access unprecedented information from a traditionally opaque sector. Those data are intimately related with the engagement Frogtek gets from its customers and the efficiency reached by it operations.

30'

Guillermo Caudevilla (Frogtek)

Coffee Break and Networking

30'

ML for Marketing & Human Resources

Seamus Abshere (Faraday)

Databases, Templates, Infrastructure: Making ML Work for B2C

Faraday is AI for B2C. They have reusable Machine Learning templates for all stages of the B2C revenue lifecycle, from acquisition to upsell to retention. Their customers have seen social ad performance comparable to the best Facebook modeling, while benefitting from cross-channel engagement and attribution. In this talk, you will learn how Faraday.io takes customer data and combines it with a proprietary national database and ML templates to help other companies acquire, upsell, and retain more customers.

30'

Seamus Abshere (Faraday)

David J. Marcus (PandoLogic)

Machine Learning and AI-Enabled Job Advertising Platform for Enterprise Recruiting

The role of machine learning (ML) in our proprietary campaign algorithms. Utilizing over 10 years’ worth of historical job performance data (with nearly 200 billion data attributes) to establish predictive-performance benchmarks that determine when, where and how each employer’s job is dynamically campaigned online, in real-time. Our ML-driven algorithms budget and optimize campaign spending in real-time across all jobs during the campaigning period. Our ML-driven algorithms significantly improve the efficiency of employer budget utilization in the aggregate and significantly increase the number of applicants for posted jobs.

30'

David J. Marcus (PandoLogic)

Patrick Coolen (ABN AMRO)

Machine Learning Delivering Business Impact in Human Resources Management

Patrick Coolen, Manager HR analytics at ABN AMRO, will be sharing the journey of the ABN AMRO analytics team in the past four years and how a big organization like this bank uses Machine Learning to discover interesting insights about their employees. This talk will cover why all companies should do analytics in HR in the first place, how to convince senior management to apply ML techniques, how to set up an HR analytics function, and finally, how ABN AMRO uses ML in HR, providing actual examples as well as the practical takeaways of the 10 golden rules of HR analytics.

30'

Patrick Coolen (ABN AMRO)

Panel of Experts

30'

Santiago Márquez (Barrabés.biz)

Opening Review Remarks

30'

Santiago Márquez (Barrabés.biz)

Real-World Use Cases

ML for Finance & Investment

Jorge Pascual (Anfix)

How ML is Disrupting the Accounting Industry

Oxford has predicted that Accountants and Auditors are going to disappear with a 99% chance. Automation and ML are going to take on almost every process. A whole industry have to be reinvented. Small and Medium Businesses represent more than 90% of the developed world's GDP and almost all of them are supported by accountants. According to Accenture, by 2020 more than 80% of traditional financial services will be delivered by cross-functional teams that include AI. Technology can do heavy lifting, number crunching, and report compilations while accountants focus on judgment-intensive tasks. In sum, ML is going to make accountants more efficient and productive.

30'

Jorge Pascual (Anfix)

Arturo Moreno (Preseries)

Upgrading Technology Financing: Machine Learning Enabler of a Data-Powered Process

The way technology is financed has not changed much in the last decades: face-to-face interactions, followed by human judgement (bias?). Most sophisticated investors adopted excel sheets to calculate ratios and KPIs (CAC, LTV, ARR, CPM, etc.) to allow for benchmarking and simple rules of thumb. The result is a relationship-driven diligence that inherently creates a high propensity for bias and a low propensity for scale. We have seen the investor community embracing the application of data and models on any other industry, yet the question remains for the majority of the industry how early-stage investment decisions can be better made with data. A growing number of investors are experimenting around data-driven strategies to early-stage investing. The names of Social Capital, EQT, GV, Correlation Ventures or InReach Ventures, to name a few, are already showing results. PreSeries is building an affordable, best-in-class tool to allow all investors leverage the benefits that data and ML represents for the generation of insights. From millions to thousands of dollars per year, for all investors. In a world where technology investors’ role transitions from “hunter to gatherer”, PreSeries believes that a data-centric culture at investing organizations will not only bring fastest and better investment decisions, but will also allow investors to be helpful to startups in a much more productive manner thanks to the insights that the analysis of their data will bring.

30'

Arturo Moreno (Preseries)

Santiago Márquez (Clluc)

Blockchain and Machine Learning. The Perfect Storm?

Blockchain and Machine Learning are two of the most disruptive technologies today are postulated as key mechanisms to change not only how organizations work but also how the Society itself is articulated. But are there any synergies between them? Is it possible to apply the ML technology to Blockchain? What is the status of this approach? Throughout this talk Santiago Márquez CTO of Clluc and Blockchain expert will try to clarify it.

30'

Santiago Márquez (Clluc)
ML for Telecom

Francisco Martín Pignatelli (Vodafone)

Predictive Mobile Networking

Telecommunication Networks are struggling to keep up with the growth of data they have to manage to deliver service to their customers. This demand is leading to paramount complexity that traditional systems can’t manage. Machine Learning and Artificial Intelligence are the best way to address this challenge, allowing also Networks to be predictive as opposed to reactive which changes how technology has worked in Radio for the last 25 years

30'

Francisco Martín Pignatelli (Vodafone)

Coffee Break and Networking

30'

How to Adopt ML in your Company

Francis Cepero (A1 Digital)

Build 100 Data Analysts in 6M through ML democratization

30'

Francis Cepero (A1 Digital)

Luis Martín (Barrabés.biz)

How to Bring ML to your Business: Few Steps from Ideas to Results

With more and more data, devices and processes expanding in time and space the current computational models will need to evolve to have more centralized and de-centralized capabilities at the same time. To make this work we need new structures in the approach to data, transactions and infrastructure.

30'

Luis Martín (Barrabés.biz)

Panel of Experts

45mins

Closing Remarks

15'

Lunch Break and Networking

60'

Poul Petersen (BigML)

Practical Machine Learning Workshop with the BigML Platform

120'

Poul Petersen (BigML)