How Machines Learn: From Examples to Models
In this hands-on workshop for Women in Computer Science (WiCS) Conference, students will explore how computers learn from examples instead of being given every rule by hand. Through interactive activities like drawing recognition, fitting a simple prediction model, and training an image classifier, students will see how machine learning models make guesses, learn from mistakes, and improve with better data. The workshop is designed for students who are curious about AI and want an accessible, engaging introduction to how modern machine learning works.
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- 200 University Ave W
- Waterloo, Ontario
- Canada N2L 3G1
- Building: Davis Centre
- Room Number: DC 2585
Speakers
Eric Yan of University of Waterloo
How Machines Learn: From Examples to Models
Biography:
Eric Yan is currently a Master’s student in the Systems and Networking Group at the University of Waterloo, advised by Dr. Raouf Boutaba. He holds a B.Sc. (Hons.) in Computer Science and a Master of Management degree from the University of British Columbia. His research focuses on systems and networks for machine learning, as well as AI-driven network management. Outside of research, Eric enjoys hiking, and one of his life goals is to climb peaks around the world.
Address:Canada
Eimaan Saqib of University of Waterloo
How Machines Learn: From Examples to Models
Biography:
Eimaan Saqib is a Masters student in the Systems and Networking Group at the University of Waterloo, supervised by Professor Raouf Boutaba. She received her Bachelors in Computer Science and a minor in Mathematics from Lahore University of Management Sciences (Pakistan) in 2024. Her research focuses on resilience and security in 5G networks.
Address:Canada
Fatemeh Shafiei Ardestani of University of Waterloo
How Machines Learn: From Examples to Models
Biography:
Fatemeh Shafiei Ardestani is a Master’s student in the Systems and Networking Group at the University of Waterloo, supervised by Professor Raouf Boutaba. She holds a B.Sc. in Computer Engineering and a minor in Mathematics. Her research focuses on applying machine learning and analytics to enable automated network management and closed-loop control in 5G networks.