With the rapid pace of connectivity and product innovations that we've seen in the past decade across the online marketplace, simple A/B testing for deciding the efficacy of an action is not enough. In the era of intense competition, it is essential that we take that leap to learn not just population summary from A/B tests, but to make inferences on personalised levels - and spend on marketing only where it's necessary.
The applications are open to all industries - especially the ones where actions need to be determined and learned from big data anlaytics - for instance, who are the people whom this product feature should be targeted to - and that “who” can be answered on an individual level by building models on test-control testing and can be applied to the overall population.
Machine Learning Takeaways: Uplift Forest Models to determine the population fit for targeting. The idea of uplift, and how it relates and compares to traditional statistical models

With the availability of new tools, the ability of models to identify nuanced data patterns that relate to credit risk has become plausible. By using the right technology /tool a strong ,robust, palatable analytical solution can be identified which can be identified which can provide significant improvement in model performance over a traditional complex models brings different challenges from usage perspective.

Balakerthy is the regional Head of retail risk models at HSBC Asia Pacific Region. He manages both regulatory risk and business risk models and is responsible for delivery of advanced analytics projects including development of machine learning and AI Models and strategies.

He has extensive experience in building and managing credit analytics function for retails and wholesale products across different markets. He has led successful interaction with regulators ,audit and independent review functions.

An introduction to different analytics concepts and approaches and explanation of their interrelations and applications.

Annie is currently working as Head of Data Science/Chief data scientist in UL. Previously, she has worked with ResMed as global advanced analytics manager and senior research scientist with Intel. She has doctorate degree in Artificial Intelligence, masters and bachelors degrees in computer science and information technologies. She has 15 years of R&D experience and worked as assistant vice president of data science, analytics manager, lead data scientist, software engineer, system analyst, researcher, research scientist and authored many technical patents, conference proceedings, journal articles and blogs. She held best student awards, and scholarships for graduate and postgraduate studies and best employee awards.

Over the past few years, we have seen increased activity in the development of software powered applications in a range of sectors, such as autonomous vehicles, security, and data mining. As the level of investment and research effort in these fields intensifies, companies are looking to protect the rights to the Intellectual Property of their innovative solutions and algorithms to protect their technology roadmap and gain an edge over their competitors. But what are the different ways we can protect these innovations? In this presentation, we will discuss the protection of software - based innovations, discuss how to deal with A.I “inventors”, and provide examples of IP considerations that need to be considered during the product development lifecycle.

Vinod Khader has around 20 years experience in Software Development and is an Associate Director at IBM Software Labs in the Data and AI division. In the current role, he leads the development of Watson Machine Learning Platform on IBM's public and private cloud platforms with teams across the globe. Watson Machine Learning platform help Data Scientists, Data Engineers and App Developers in managing the end to end Machine Learning and Model management life cycle starting from Training, Evaluation and Serving.

The intro to AML,setting up the environment , building a regression model , classification modelling to regression and analyzing results

1. I'll talk about different types of Computer Vision,
2. Dive a little deeper into how 'Deep Learning' CNNs work,
3. Compare some of the 'state of the art' Computer Vision algorithms, give a rough guide on how to choose which algorithm to use and finally
4. Consider the ethics of Computer Vision.

My life is machine learning R&D, currently being applied in CIB banking innovation and disruptive technologies research.

With the rapid pace of connectivity and product innovations that we've seen in the past decade across the online marketplace, simple A/B testing for deciding the efficacy of an action is not enough. In the era of intense competition, it is essential that we take that leap to learn not just population summary from A/B tests, but to make inferences on personalised levels - and spend on marketing only where it's necessary.
The applications are open to all industries - especially the ones where actions need to be determined and learned from big data anlaytics - for instance, who are the people whom this product feature should be targeted to - and that “who” can be answered on an individual level by building models on test-control testing and can be applied to the overall population.
Machine Learning Takeaways: Uplift Forest Models to determine the population fit for targeting. The idea of uplift, and how it relates and compares to traditional statistical models

Using a sample dataset and a live demo, see how quick and easy it is get started with Machine Learning in Splunk.

Gregg Woodcock is a gun-toting, Christian, homeschooling father of 3 whose 25+ years of IT experience (primarily in Telecom) and early adoption of Splunk (v3) has positioned him on the leading edge of the Big Data explosion and uniquely qualified him to launch Splunxter, currently headquartered near Dallas in Texas. He is a charter member of the "Splunk Trust" and the founder and chairman of the Dallas-area Splunk User Group, as well as a frequent guest-speaker at Telecom & Big Data events, an occasional street-preacher, the former Chairman of the Constitution Party of Texas, and a top-contributer & moderator at Splunk's Help Forum (http://answers.splunk.com). He is a genuine evangelist of all the best things in life and of course that includes Splunk!

What are the benefits of data driven marketing? Which ML techniques are relevant to specific stages of relating with the customer? How do you convert data to actionable insights? What are the biggest challenges to achieving success?

In a hyper-competitive economy, data-driven marketing is crucial to the success of a company. Come learn the strategies and ML techniques necessary to thrive in a customer-centric field.

Dr. Savitha Namuduri, Ph.D. has over 20 years of experience in Data and Analytics space, spanning over several industries such as Telecom, Utility, Retail, Healthcare and CPG. Her analytical skill sets range from developing visualizations, to customer segmentation to building predictive models and democratizing data science techniques to solve day-to-day business problems. Savitha's career has been focused on marketing analytics for the past 10 years running Analytics teams for CRM, media marketing and Customer Journey. In addition to visualization and data analysis, Savitha also focuses on providing data-driven recommendations for strategy, setting up experimental designs, conducting primary and secondary research, and developing learning plans to inform marketing and sales strategies. She brings a unique blend of business acumen and technical skill set to consult with clients on the best marketing practices and their measurement.

In this use case, we will use IBM Watson Studio to examine the costs associated with 1481 cardiac patients. In the data set, each patient is unique, each heart treatment is unique and each physician managing the treatment is unique. Despite this, is there a way to determine if any of these treatments cost more than it should have? That is, can we come up with a smart method to identify procedures that may represent fraud or abuse.

Karan is the Director for Watson and AI Applications in Asia Pacific. His current mission is helping customers unlock value from their data using Watson and IBM AI applications in combination of open source and partner ecosystem. With over a decade of experience in Data and AI senior leadership and executive management positions, Karan has delivered sustained growth consistently in business through innovative customer and channels strategy. Prior to IBM, Karan also held executive level positions at Cloudera, Informatica and HCL Technologies.

A technology and business thought leader, Karan has been the keynote speaker for many events across Asia Pacific and USA. Karan has been named IBM cloud engagement champion to inspire and engage more than 10000+ employees of IBM Cloud Asia Pacific. He is the founding member of Data and AI forum and has been featured in leading company blogs and publications.

NLP is a one of hot topics in ML/AI area at the moment. Many companies and organizations started to apply NLP on real life problems. Auto clustered topics is one approach to better understand how information are clustered. This is useful for trend analysis as well. However due to unsupervised approach in nature, the auto generated topics can hard to interpret. In this talk we discuss the problem and few approaches we may apply to improve the interpretability of unsupervised approach.

Experienced Lead Data Scientist with a demonstrated history of working in the banking industry. Skilled in Problem solving,Data Analytics, Automation and Management. Strong engineering professional with a Doctor of Philosophy (PhD) focused in Software Engineering (AI - intelligent agent system) from University of Melbourne.