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Track: AI/Machine Learning without a PhD

Location: Whittle, 3rd flr.

Day of week: Monday

AI/ML is more approachable than ever. Discover how deep learning and ML is being used in practice. Topics include: TensorFlow, TPUs, Keras, PyTorch, & more. No PhD required.

Track Host: Martin Goodson

Chief Scientist/CEO @EvolutionAI

Martin Goodson is the chief scientist and CEO of Evolution AI, which develops a platform for large-scale natural language processing. Martin has designed machine learning products that are in use at FTSE 100 and Fortune 500 companies like Dun & Bradstreet, Time Inc., Royal Bank of Scotland, and Condé Nast. Previously, Martin performed machine learning research at the University of Oxford. He runs the largest community of machine learning practitioners in Europe, Machine Learning London, and convenes the Royal Statistical Society roundtable, AI in Financial Services. Martin’s work has been covered by publications such as the Economist, Quartz, Business Insider, TechCrunch, and others.

Find Martin's blog on data science here:

10:35am - 11:25am

How to Prevent Catastrophic Failure in Production ML Systems

AI systems can fail catastrophically and without warning, a characteristic not welcomed in the corporate environment. Martin will describe the unpredictable nature of artificial intelligence systems and how mastering a handful of engineering principles can mitigate the risk of failure. You’ll learn the kinds of errors artificial intelligence systems make, how to build systems that protect against common errors, and why evaluation can be much harder than it seems.

Martin Goodson, Chief Scientist/CEO @EvolutionAI

11:50am - 12:40pm

Test Driven Machine Learning

Software engineers are familiar with test driven development, but are not familiar with the statistical testing required in machine learning. Machine learning specialists are familiar with testing during the model building phase when they withhold data for cross-validation or final testing, but they are unfamiliar with software engineering principles. While testing a learned model gives an idea how well it might perform on unseen data it is not sufficient for model deployment. Trying to learn from test driven development practices we are looking across the machine learning life cycle to understand where we need to test and how this can be done. The testing of data, for example, is essential as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment. Testing the decisions made by a deployed machine learning model is equally important to understand if it delivers the expected business value. 

Detlef Nauck, Chief Research Scientist for Data Science @BTGroup and Visiting Professor @bournemouthuni

1:40pm - 2:30pm

Intuition & Use-Cases of Embeddings in NLP & Beyond

Machine Learning has achieved tremendous advancements in language tasks over the last few years (think of technologies like Google Duplex, Google Translate, Amazon Alexa). One of the fundamental concepts underpinning this progress is the concept of word embeddings (using something like the word2vec algorithm). Embeddings continue to show incredible power for representing words in a way that machines can use to do some very useful things by solving complex language problems. More recently, companies like Airbnb and Alibaba have started using the concept of embedding to empower non-NLP use-cases like recommendations, search ranking, and personalization.


In this talk, we will go over the intuition of word embeddings, how they're created, and look at examples of how these concepts can be carried over to solve problems like content discovery and search ranking in marketplaces and media-consumption services (e.g. movie/music recommendations).

Jay Alammar, VC and Machine Learning Explainer @STVcapital

2:55pm - 3:45pm

AI/Machine Learning Open Space

Shane Hastie, Director of Agile Learning Programs @ICAgile

4:10pm - 5:00pm

Understanding Deep Learning

No matter what your role is, it is really important to have some understanding of the models you’re working with. In last year's keynote, Rob Harrop talked about the importance of intuition in machine learning. This is a step towards that.

You might already be using neural networks. How can you go beyond just using deep learning and move towards understanding it so you can make your models better?


Deep learning is notoriously opaque, but there are principles behind how neural networks are constructed that can shed a lot of light on how they behave.


The goal of this talk is to help you understand foundational concepts about neural networks that are not often taught in online tutorials (and that even data scientists may not know), so you can better design and deploy neural networks.


We will go from

  1. Dissecting a single layer of a neural network to

  2. How to train (multi-layer) neural networks to

  3. Problems with training very deep networks and how you can tackle them.


At every stage, I will highlight key things to pay attention to, such as learning rates and how to initialise your network. These will all be related to how the networks are constructed and trained, so you can understand why these parameters are so important.


I will end the talk with practical takeaways used by state-of-the-art models to help you kickstart building powerful neural networks.

Jessica Yung, Machine Learning blogger and entrepreneur, Self-Driving Car Engineer Scholar @nvidia

5:25pm - 6:15pm

H2O's Driverless AI: An AI that creates AI

Through my kaggle journey to the top spot, I have noticed that many of the things I do as a data scientist can be automated. In fact automation is critical to achieve good scores and promote accountability, ensuring that common pitfalls in the modelling process are prevented. Through automation, data science can be democratized and reach a bigger audience.


In this talk I will share our approach on automating machine learning using H2O’s Driverless AI:

Driverless AI employs the techniques of expert data scientists in an easy-to-use application that helps scale data science efforts. Driverless AI empowers data scientists to work on projects faster using automation and state-of-the-art computing power from GPUs to accomplish tasks in minutes that used to take months.

Marios Michailidis, Competitive Data Scientist @h2oai

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