everything.study
STAT 385

Introduction to Neural Networks

3 credits · Undergraduate

Winter 2026

Topics

  • Predictive analytics, supervised learning and cross-validation
  • Numerical overflow and underflow
  • Feed-forward networks and universality
  • Classification and regression architectures
  • Objective functions and parameter estimation
  • Gradient descent, backpropagation and Hessian computation
  • Initialization and adaptive learning rates
  • Regularization, augmentation and noise injection
  • Recurrent and long short-term memory networks
  • Convolutional networks
  • Python or R implementation and applications

Readings

  • Reference (not explicitly required): C. C. Aggarwal. Neural Networks and Deep Learning: A Textbook, 2nd edition. Springer, 2023.
    Listed reading
  • Reference (not explicitly required): C. M. Bishop and H. Bishop. Deep Learning: Foundations and Concepts. Springer, 2024. https://www.bishopbook.com/
    Listed reading
Winter 2026 · STAT 385

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    Listed reading; requirement unspecified
Winter 2025 · MACF 491

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    Listed reading; requirement unspecified
Winter 2025 · STAT 385

Readings

  • Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.
    Listed reading; requirement unspecified
  • Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.
    Listed reading; requirement unspecified
Sources