Core: Fall 2019

You always start with the basics, and in this meeting we are doing just that! We will be going over what Machine Learning consists of and how we can use models to do awesome stuff!

In this lecture, we explore powerful yet lightweight models that are often overlooked. We will see the power of combining multiple simple models together and how they can yield amazing results. You won't believe how easy it is to classify with just a line!

You've heard about them: Beating humans at all types of games, driving cars, and recommending your next Netflix series to watch, but what ARE neural networks? In this lecture, you'll actually learn step by step how neural networks function and learn. Then, you'll deploy one yourself!

You know what they are, but "how do?" In this meeting, we let you loose on a dataset to help you apply your newly developed or honed data science skills. Along the way, we go over the importance of visulisations and why it is important to be able to pick apart a dataset.

Ever wonder how Facebook tells you which friends to tag in your photos, or how Siri can even understand your request? In this meeting we'll dive into convolutional neural networks and give you all the tools to build smart systems such as these. Join us in learning how we can grant our computers the gifts of hearing and sight!

This lecture is all about Recurrent Neural Networks. These are networks with memory, which means they can learn from sequential data such as speech, text, videos, and more. Different types of RNNs and strategies for building them will also be covered. The project will be building a LSTM-RNN to generate new original scripts for the TV series “The Simpsons”. Come and find out if our networks can become better writers for the show!

GANs are relativity new in the machine learning world, but they have proven to be a very powerful model. Recently, they made headlines in the DeepFake network, being able to mimic someone else in real time video and audio. There has also been cycleGAN, which takes one domain (horses) and makes it look like something similar (zebras). Come and learn the secret behind these type of networks, you will be suprised how intuitive it is! The lecture will cover the basics of GANs and different types, with the workshop covering how we can generate human faces, cats, dogs, and other cute creatures!

We all remember when DeepMind’s AlphaGo beat Lee Sedol, but what actually made the program powerful enough to outperform an international champion? In this lecture, we’ll dive into the mechanics of reinforcement learning and its applications.

It's easy enough to navigate a 16x16 maze with tables and some dynamic programming, but how exactly do we extend that to play video games with millions of pixels as input, or board games like Go with more states than particals in the observable universe? The answer, as it often is, is deep reinforcement learning.

We've learned about linear and statistical models as well as different training paradigms, but we've yet to think about how it all began. In Cognitive Computational Neuroscience, we look at AI and ML from the perspective of using them as tools to learn about human cognition, in the hopes of building better AI systems, but more importantly, in the hopes of better understanding ourselves.