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Exploring the capacity of using Neural Networks and Algorithms to dynamically schedule University of Toronto student schedules.

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PlanIt

Exploring the capacity of using Neural Networks and Algorithms to dynamically schedule University of Toronto student schedules.

Findings: Implemented RNN can predict class difficulty with 5% error using minimal data input.

Currently in the process of researching alternative solutions to RNN's and to establish the connection between the web-interface and the backend.

Contributers

Michelle Collins - Machine Learning, Algorithms, and Front End

Stone Yang - Backend and Database

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Exploring the capacity of using Neural Networks and Algorithms to dynamically schedule University of Toronto student schedules.

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