AI in Action October#2 Meetup @Wayfair

Hi Everyone! And welcome to the 14th AI in Action Meetup of the year!

I am delighted to have Wayfair as our host and sponsors for this event and everyone is welcome to join.

In this event, we will have a live workshop on how to build your own image classifier so make sure you bring your laptop with you would like to take part.

*** Food and beverages will be provided ***

Agenda for the evening:
6:00pm - 6:30pm - Registration/ Networking/ Refreshments
6:30pm - 6:35pm - Introduction from Anthony
6:35pm - 7:00pm - Boyan Angelov & Thomas Nguyen
7:00pm - 7:30pm - Jekaterina Kokatjuhha
7:30pm - 8:00pm - Dr. Tristan Behrens

Boyan Angelov & Thomas Nguyen
Title: xAI: Explanations and Opportunities
Abstract:
Modern machine learning algorithms tend to behave as black boxes. We have traded understanding of the inner workings of a model for increases in accuracy and performance. In the era of GDPR and global concerns about data privacy and algorithm bias, understanding how a machine learning model makes a decision has increased importance. Historically there have been numerous efforts in the field, and it is still rapidly developing. This talk is a tour of what recent xAI (explainable AI) methods and associated open source tools are available, how they work and some practical stack integration advice.

Jekaterina Kokatjuhha
Title: Building data science project from scratch: analysis of Berlin rental prices
Abstract:
This talk is about how to design a good data science project from scratch based on a real-world dataset. As a showcase project, we analyze the rental prices for apartments in Berlin. This talk will guide you through all the steps of a short-term data science project: motivation, extraction of data from the web, cleaning, and engineering of features using external APIs, storytelling, and building machine learning models. We will dive into the pitfalls and design patterns when scraping data from the web. The importance of the interactive dashboards should not be understated as they help you find useful insights on your own. We will apply the human judgment of the apartment’s address to engineer new features using google API and use correlated features to impute the feature of interest. In the end, several machine learning models will be used to explore the idea of bagging and of stacked models.

Dr. Tristan Behrens
Title: Music Composition augmented by Deep Learning.
Abstract:
"Not many people know what Garry Kasparov did after losing all matches against the chess computer Deep Blue in 1997. Kasparov focussed on a special chess league where a human with a computer plays against another human with a computer. This is a prime example of AI-augmented human intelligence. Dr. Tristan Behrens believes that we are going to see this phenomenon in more and more use cases in the near future. It is not a question of how AI is going to make humans obsolete. The question is how human endeavors could be elevated using AI technology. The creative domains are an excellent example. Tonight, Tristan will show how this is possible in music. Non-experts can be enabled to compose music in the style of Johann Sebastian Bach using Deep Neural Networks."