Neural Networks and Deep Learning
An introduction to neural networks and deep learning. Backpropagation and automatic differentiation. Architectures: convolutional networks and recurrent neural networks. Methods for improving optimization and generalization. Neural networks for unsupervised and reinforcement learning.
Prerequisites & course information
Prerequisite: CSC311H1 / CSC311H5 / CSCC11H3 / STA314H1 ; MAT235H1 / MAT235Y1 / MAT237Y1 / MAT257Y1 / MAT257Y5 / MAT232H5 / MAT233H5 / MATB41H3 ; MAT223H1 / MAT240H1 / MAT223H5 / MATA23H3 Prerequisite for Faculty of Applied Science and Engineering students: ECE421H1 / ROB313H1 ; MAT291H1 / MAT294H1 / AER210H1 / MIE230H1 ; MAT185H1 / MAT188H1; Prerequisite:; Exclusion: CSC321H5 , CSC413H5 . NOTE: Students not enrolled in the Computer Science Major or Specialist program at A&S, UTM, or UTSC, or the Data Science Specialist at A&S, are limited to a maximum of 1.5 credits in 300-/400-level CSC/ECE courses.; Exclusion:
Sources
- Course catalogue ↗2026-2027