This week John will lead a discussion on Bayesian. In Artificial Intelligence, the use of Bayes, and probability-based methods in general, has grown from a fringe area to the mainstream. This session will reveal the origins in Bayes networks in the 1980s, show how it contributed to machine learning in the 1990s, and review some of the prominent methods that are in use today.
We also want to thank Junling for her overview on sentiment analysis during last week’s meetup.
For those new to this group, the purpose of this meetup is to learn from each other the various aspects of machine learning. Our focus will be on intuitions, theories, and implementations behind machine learning algorithms. The format will be a chosen algorithm or topic reviewed/explained by a pre-assigned member through a highly interactive discussion session, and it is expected that other members will draw from their respective knowledge and experience to contribute to the explanation on the topic. We may also discuss specific machine learning projects should time permits after the chosen topic discussion.
The background of attendees varies from those that have exposure to machine learning, whether through Andrew Ng’s Coursera class or through other learning channels, to those that use machine learning in their day-to-days. However, even if you have no exposure to but have interest in machine learning, you are welcome to attend the meetup. Of course you might get more out of it if you go through the videos in Andrew Ng’s free Coursera class or equivalent.