• teaching
• deep learning
• reinforcement learning
• cyber security

Note: I like to keeps the slides fairly minimal and talk a lot during the lectures. If you have missed a lecture, please listen to the recordings.

Deep Learning

• Lecture 1: Introduction. (slides, video)
• Lecture 2: Mathematical principles and backpropagation. (slides, video)
• Lecture 3: PyTorch programming: coding session. (video)
• Lecture 4: Designing models to generalise. (slides, video)
• Lecture 5: Generative models. (slides, video)
• Lecture 6: Adversarial models. (slides, video)
• Lecture 7: Energy-based models. (slides, video)
• Lecture 8: Sequential models: by Sam Bond-Taylor. (slides, video)
• Lecture 9: Flow models and implicit networks. (slides, video)
• Lecture 10: Meta and manifold learning. (slides, video)

Reinforcement Learning

• Lecture 1: Foundations. (slides, video)
• Lecture 2: Markov decision processes. (slides, video)
• Lecture 3: OpenAI gym: by Adam Leach. (video)
• Lecture 4: Dynamic programming. (slides, video)
• Lecture 5: Monte Carlo methods. (slides, video)
• Lecture 6: Temporal-difference methods. (slides, video)
• Lecture 7: Function approximation. (slides, video)
• Lecture 8: Policy gradient methods. (slides, video)
• Lecture 9: Model-based methods. (slides, video)
• Lecture 10: Extended methods. (slides, video)

Cyber Security

• Lecture 1: Introduction. (slides)
• Lecture 2: Applied cryptography. (slides)
• Lecture 3: OS security and access control. (slides)
• Lecture 4: Network and web security. (slides)
• Lecture 5: Database security. (slides)
• Lecture 6: The threat landscape. (slildes)
• Lecture 7: Security in industry: by Jonathan Frawley (slides)
• Lecture 8: Understanding the platform. (slildes)
• Lecture 9: Software security. (slides)
• Lecture 10: Risk and ML applications. (slides)

Lab content:

• Practical 1: Hacking and securing a simple web server. (pdf, materials)
• Practical 2: Traversal and XSS attacks on a web server. (pdf, materials)
• Practical 3: Database security. (pdf, materials)
• Practical 4: Software security. (pdf)
• Answers 1: Sample answers for some of the tasks in practical 1. (pdf)
• Answers 1: Sample answers for some of the tasks in practical 2. (pdf)
• Answers 3: Sample answers for some of the tasks in practical 3. (pdf)

All the above lecture material is made available under the CC-BY-NC-4.0. Some content within the lectures is from third parties and not covered by the license. Reference as follows, e.g. for for DL and RL:

@misc{willcocks2021lectures,
  author       = {Chris G. Willcocks},
  title        = {Lectures on Deep Learning and Reinforcement Learning},
  year         = {2021},
  howpublished = {\textsc{url:}~\url{https://cwkx.github.io/teaching.html}}
}