Over the last decade, machine learning has undergone a philosophical Renaissance through the innovation of a set of computational models and algorithms often referred to as Deep Learning. These ideas have led to concrete advancements in long-standing applied domains such as classification and time-series prediction. But the real excitement over Deep Learning lies in its yet untapped potential. This course will introduce some of the core technical concepts within Deep Learning and explore how these emerging capabilities will transform the next generation of computing interfaces such as search engines, intelligent assistants, connected homes and open-world video games. In the first half of the semester, we will use class time and weekly incremental programming exercises to explore the underlying theory and key algorithms of machine learning as well as some of the more abstract insights offered by Deep Learning into vexing phenomenological questions like:
Why do we replay and reconfigure memories in our dreams?
Why do we use only a small portion of our brains at any given time?
Why can we catch a baseball without being able to recite Newton’s equations?
And most importantly, what defines learning as a phenomenon?
In the second half of the semester, we will look at the emerging applications of these technologies to art, design and toolmaking, culminating in final projects that relate the techniques studied in this course to any field of human-computer interaction.

BY Patrick Hebron