THIS IS AN OLD PROCEDURE. PLEASE SEE THE NEXT POST FOR CURRENT INFORMATION.
CSC321H1S: Introduction to Neural Networks and Machine Learning (Winter 2018)
In order to apply, you will need to have met the following official prerequisites:
Arts & Science Students:
- Calculus: (MAT136H1 with a minimum mark of 77)/(MAT137Y1 with a minimum mark of 73)/
(MAT157Y1 with a minimum mark of 67)/MAT235Y1/MAT237Y1/MAT257Y1- Linear Algebra: MAT221H1/MAT223H1/MAT240H1
- Probability: STA247H1/STA255H1/STA257H
Engineering Students:
- Calculus: AER210H1, MAT292H1
- Linear Algebra: MAT185H1
- Probability: STA286H1
The Balloting Procedure
To apply, you must submit the following two documents:
- a PDF of your most recent unofficial transcript from ROSI/ACORN (i.e. a full academic history)
- a PDF with answers to the Ballot Questions (see below).
A CV is not required
Ballot Questions
- Are you an Engineering student?
- What is your name and student number?
- What is your University of Toronto email address?
- Which section are you interested in taking? Day (T1, R1-3) or Evening (T6-9)?
Please answer questions 5-7 with one paragraph each:
- Why are you taking this course, and what are you hoping to get out of it?
- Briefly describe a programming project you have worked on (not necessarily machine learning related).
- If you have not met one of the above prerequisites, describe other relevant background you have and how it will prepare you for the course. (If you have met all the prerequisites, you may just write, “I have met all the prerequisites.”)
- Submit these two documents here by October 15, 2017. Decisions will be released by December 4, 2017.
We recommend all students enrol into their 2017-18 courses as though their ballot was refused. If a space in CSC321H is offered to you, we will contact you and you can then drop your “back-up” course.