THIS COURSE IS CURRENTLY FULL – Will be offered again in Winter Semester.

8 Classes

Time: 7:30-9:30pm (Tuesdays)

Dates: October 9, 16, 23, 30 | November: 6, 13, 20, 27

Location: 3501 Peel Street, Montreal (Main Floor)

Price: $65

Course Description: Artificial intelligence or machine learning is becoming an increasingly large part of our everyday society. Yet, the artificial intelligence we see in pop culture commonly does not reflect reality. In this course, we will dive into how artificial intelligence really works. We will build our own artificial intelligence model, and examine the strengths and weaknesses of various approaches. We will see why we probably do not need to worry about SkyNet, at least for the next couple decades. But, we will look at why ethics are a pressing issue in artificial intelligence, and what we can do about it. We will examine the application of artificial intelligence to various fields, with the goal of giving participants the tool set to begin applying cutting edge technologies to their own personal projects. Participants will also build an understanding of how to work with artificial intelligence models already existing in their fields, and where artificial intelligence can and cannot make an impact.

About the Instructor: Erik Partridge, McGill ‘18, is a Data Scientist at Mosss, a San Francisco-based technology corporation. Presently, he applies machine learning to a variety of cutting-edge problems in areas ranging from computer vision to natural language processing (he makes computers see and talk). Erik has been programming for the last decade in various capacities, including in assorted computer science courses at McGill and start-ups. Recently, he published an article Improving on Q & A Recurrent Neural Networks Using Noun-Tagging as part of a team, which set a new bar for state of the art in teaching computers how to understand questions. Erik is no stranger to the McGill community, he graduated in May of 2018, up until which time he served as the President of the Arts Undergraduate Society.

Materials Required: Laptop, ideally MacOS or Linux. Windows users can follow along, but may require some adjustments.

Suggested Materials: Notebook and pen (if desired). 

Required Knowledge: A basic knowledge of any programming language, Python preferred.

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