Hey everyone!

The goal of a classical machine learning problem usually falls in one of the two categories:

1. What can I learn from the data I currently have?
2. Based on observed data, can I accurately predict the occurrence of some activity in the future?

In this webinar, we will be focussing on one such model in statistical learning, which aims to find a predictive function for a given dataset - the Artificial Neural Network. We will be taking a deep dive into ANNs, covering:

1. What is a Neural Network? What are its statistical components? How do they work?
2. What are the different kinds of Artificial Neural Networks?
3. Example use cases?

The webinar will be hosted by Vani Asawa, a software engineer at Microsoft who is currently working with the Threat Intelligence team in the United Kingdom. Prior to joining Microsoft, she was an intern at the Oxford Robotics Institute, where she was researching path planning algorithms for autonomous systems. She was also a data analyst intern at Riscure, a global cybersecurity lab offering security solutions in the Netherlands.

She holds a masters degree in Mathematics and Statistics from the University of Oxford.

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