Information and materials for courses I teach.

Deep Neural Network (Software Engineering Programme)

This course will aim to introduce students to the core fundamentals of modern deep multi-layered neural networks, while still remaining grounded in practice. The underpinning assumption in its design is that while students may have experience (especially hands-on experience) in machine learning, data science or general software engineering — they have not worked with deep learning or taken prior courses in the area.

At the conclusion of this course students should understand:

  • The principles and approaches for learning with deep neural networks.
  • The main variants of deep learning (such convolutional and recurrent architectures), and their typical applications.
  • The key concepts, issues and practices when training and modeling with deep architectures; as well as have hands-on experience in using deep learning frameworks for this purpose.
  • How to implement basic versions of some of the core deep network algorithms (such as back-propagation)
  • How deep learning fits within the context of other machine learning approaches, and what tasks it is considered to be suited and not well suited to perform.

Where: Department of Computer Science, Oxford University.

Next Iterations: 21/11/2022.

More Details: SEP-Deep Neural Networks