This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:

• Part I covers the fundamentals of machine learning, and,
• Part II demonstrates the applications of various machine methods in solving a real world problem.

Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.

Level: Introductory to Intermediate

Length: Two 3-Hour Sessions

Format: Lecture + Hands-on

Prerequisites:

• Alliance Account
• Data preparation or equivalent knowledge.
• Basic Python knowledge and experience.
• Knowledge and experience with Tensorflow and Scikit-learn would also be helpful. :: Tues. June 11 ::
  09:00 to 12:00
  13:30 to 16:30

Registration link

Compute Ontario Summer School is a series of online courses on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software. It runs from June 3 to June 21, 2024. The courses are delivered each workday from 9:00am to 4:30pm (EDT) with a lunch break,  in two parallel streams. Pick-and-choose the course(s) you want to attend. Registration is free. Please register early as  courses have a limited capacity. The Summer School is jointly delivered by SHARCNET, SciNet, Centre for Advanced Computing, in collaboration with the Alliance and RDM experts from across Ontario and Canada.