Introduction to Artificial Intelligence/Machine Learning

Black Boys Code Canada

The purpose of this workshop is to introduce and prepare boys who have decided to pursue a university degree in computer science. Typically, this introductory course is given students in the second year of a four-year university computer science degree. The workshops will primarily focus on classification tasks for publically available image datasets, such as MNIST and CIFAR10. It will also cover li  near algebra and calculus, which serve as the basis of Machine Learning:
Day 1: Into the introduction of Machine Learning. The first day will be dedicated to introducing the current and future applications of machine learning. This will provide examples such as self-driving vehicles and programs learning to play video games. We will also focus on mathematical prerequisites for machine learning. This will mostly involve introductory calculus, linear algebra and simplified lessons which will focus on providing an intuitive understanding of mathematical concepts. Lastly, we will focus on core machine learning concepts that are ubiquitous within the current field of research while avoiding mathematically heavy proofs. The first section will be dedicated to enforcing foundational machine learning concepts. The second stage will then consist of learning how to program neural networks that help facilitate machine learning tasks. The section will be spent towards teaching the use of machine learning libraries. The python language libraries will be Tensorflow and Numpy. Tensorflow is a Google founded machine learning python library that is largely responsible for most of the current state of the art neural networks. By the end of the day, it is expected that students learn how to build simple neural networks using these libraries. Their first networks will likely train on MNIST data, as it is one of the most basic datasets publicly available.
 Day 2: The final day will involve creating simple neural network models that are used for classification and perhaps other tasks like segmentation. Segmentation is classification of on the pixel level while classification tasks happen on the image level. If time permits, time series machine learning will also be covered due to its wide application in fields like finance, social science and healthcare. All hands on tutorials will involve the instructor(s) working with students to ensure that their models are working properly. The final objective is to have each of the students train their models on the publically available datasets aforementioned.

University of Toronto Campus

Our {communityGuidelinesLink} describe the sort of content we prohibit on Eventbrite. If you suspect an event may be in violation, you can report it to us so we can investigate.

To report other categories of prohibited or illegal content, submit {link}

Introduction to Artificial Intelligence/Machine Learning

More events from Black Boys Code Canada

Discover more events from Black Boys Code Canada, from Science & Tech to other experiences you might love.

Still looking for the right event?

Explore all events in Toronto and filter by date, category, and more to find the perfect fit.

Introduction to Artificial Intelligence/Machine Learning

More events from Black Boys Code Canada

Discover more events from Black Boys Code Canada, from Science & Tech to other experiences you might love.

More Science & Tech events

Browse more Science & Tech events with different dates, prices, and formats to find your next great experience.

Still looking for the right event?

Explore all events in Toronto and filter by date, category, and more to find the perfect fit.