The emergence of Data Science is considered to be a great advance in the domain of modelling. The abundance and the explosive growth of recorded data in recent years has added a new dimension to the established paradigms of theoretical, empirical and computational modelling; these are now augmented by data driven modelling. Data Science encompasses the established domains of data warehousing, data mining, cluster analysis, pattern classification, machine learning and data visualisation. The application of Machine Learning in general and Deep Learning in particular, to very large data sets, has led to ground-breaking progress in recognising patterns of sounds, images, & data.
The human brain finds it difficult to make sense out of very large volumes (peta bytes) of unstructured data. Yet Deep Learning can discover hidden ‘connections and patterns’. Humans are not able to model these ‘connections and patterns’ because their cognition power limits them in making sense of these very large data sets and their inherent complexities.
This event brings together experts from industry and academe to explore and discuss the major advances and applications of these technologies.
Come along and open up to the new era of AI, Machine Learning and Deep Learning and find out how this can help you and your organisation in an increasingly ‘data-driven world’.
In addition to the conference, there are four pre- and post-conference workshops:
a. In depth overview of [Deep Learning / Machine Learning and Ensemble Learning; how they relate to each other]
b. Hands on workshop on Tensorflow
c. AI in Robotics (including IOT)
d. AI in Multimedia – capturing sound, data and image
Who should attend
We are looking for Use Case Presentations on AI, Machine Learning and Deep Learning, particularly in the industry sectors of
and others. If you wish to submit a proposal to present at this event please fill in the speaker’s response form.
Karlijn Willems, Data Science Journalist at Datacamp
Data science requires more than traditional Integrated Development Environments (IDEs) can offer: the need to create and share data stories. That's why data scientists often resort to notebooks. In her talk, Karlijn Willems will guide you through the landscape of data science notebooks, from Jupyter to Beaker to R Markdown to Zeppelin and more, providing a comparison between the different notebooks that are out there for data science enthusiasts!
Bogdan Ciubotaru, CTO, Everseen Ltd
♦ Process efficiency and integrity represent two main factors impacting many sectors including retail, manufacturing and transportation
♦ Vision has always been a critical information source, however it is mainly specific to human observers and hence difficult to use effectively
♦ Machine vision and artificial intelligence open the door for a wide range of applications including efficient process management
♦ The positive impact on various businesses and industries has already been proven with great growth envisioned for the future
Mandie Quartly, Worldwide Lead, Machine Learning and High Performance Analytics software, IBM
There’s a lot of talk about using Artificial Intelligence (AI) to gain business insights, but there are a number of key ingredients required to actually make it happen in a timely fashion. A vital element is the technology which underpins AI and machine learning applications. Come and hear more about "making it happen" for your organisation; learn how to take advantage of rapidly evolving and innovating technologies. The emphasis is very much on real world use cases encompassing retail and financial markets.
Radek Maciaszek, CTO, A4G
Real time bidding is a dominant way or purchasing volumes of online advertising inventory. What was yesterday a domain of high-frequency trading hedge funds became a norm in todays advertising world. I will talk about the fascinating world of RTB and will discuss the business cases as well as technological challenges advertising companies face today.
Ben Houghton, Senior Data Scientist, Advanced Data Solutions (ADS), Barclays
Data Science is fundamental in driving data-led decisions in a multitude of business areas. In this talk, we will discuss the breadth of opportunities for data science available in the finance sector and the methodologies we can employ to make smart decisions. We will also discuss how data science can be made accessible to everyone, not just data scientists.
Timo P Kunz, Data Scientist, catawiki
In a time where AI is the only game in town, traditional methods seem to vanish from the analyst’s toolbox. This talk describes how catawiki uses stochastic modelling and simulation approaches to better understand customers’ behaviour on its auction platform.
Konstantinos (Kostas) Perifanos, Lead Machine Learning Engineer, Argos.co.uk
Demand forecasting for new products is not an easy task. The challenge is how to forecast sales for a product in case of lack of data. Given a database of several hundreds of thousand products and their demand history, we developed an Information Retrieval / Machine Learning approach to solve this problem.
Elisabetta Fersini, Assistant Professor - University of Milano-Bicocca Italy
Nowadays the number of people consulting product reviews on the web before making any purchase has increased. If most reviews are positive the probability of buying a given product increases, while if most reviews are negative almost certainly customers will choose another product. This gives strong incentives for opinion spamming, which refers to the writing fake reviews to intentionally mislead readers. In this talk, we will give an overview of the current methodologies for fake review detection, extending a markov random field model with some concepts about "burstiness of reviews".
John Murray, Data Scientist & Academic Researcher, University of Liverpool/Fusion Data Science
The growth in GPS fitted smartphones, smartcards and other location aware technology, adds an extra dimension to consumer analytics. John Murray will discuss the role of spatial data in gaining additional insights and show, with practical examples, how it can be integrated into your customer database.
Guy Kirby, UK Head of Advanced Analytics, North Highland Consulting
How can companies move away from widely-targeted marketing campaigns and use their existing data more intelligently to laser focus their target on those most likely to convert? This talk looks at how North Highland worked with Express Oil to build a predictive model using 1st party data collected across 350 variables and 3rd party data from over 2,000 variables. Guy will outline how North Highland identified target customers most likely to convert to mechanical services, allowing Express Oil get 7:1 ROI in the first 6 months, convert thousands of customers and generate significant new revenue not previously possible.