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 Encore recordings.
Deep Learning
- Lecture 1: Intelligence and learning - from nature to machine. (slides)
- Lecture 2: PyTorch Programming - live programming tutorial with minimal slides. (slides)
- Lecture 3: Building and improving architectures. (slides)
- Lecture 4: Neural Networks and Backpropagation: by Grégoire Payen de La Garanderie. (slides, handout)
- Lecture 5: Density Estimation and Generative Models. (slides)
- Lecture 6: Advances in Generative Models. (slides)
- Lecture 7: Reinforcement Learning. (slides)
- Lecture 8: Recurrent Neural Networks: by Phil Jackson. (slides)
- Lecture 9: Clustering and Manifold Learning. (slides)
- Lecture 10: Learning to Learn within the bigger picture. (slides)
Deep Learning Teaching Materials
- PyTorch lecture materials
- Simple network on FashionMNIST
- CNN on CIFA100
- Simple GAN
- Wasserstein GAN
- Variational Autoencoder
- UNet
Cyber Security
- Lecture 1: Introduction, history, today, and terminology slides. (slides)
- Lecture 2: Applied cryptography slides. (slides)
- Lecture 3: Operating system security and access control slides. (slides)
- Lecture 4: Identification, authentication, and authorization: by Grégoire Payen de La Garanderie*. (slides)
- Lecture 5: Network and web security slides. (slides)
- Lecture 6: Database security slides. (slides)
- Lecture 7: The art of cyber security - the threat landscape and tactics. (slildes)
- Lecture 8: Software security - understanding the platform slides. (slildes)
- Lecture 9: Security in industry: by Jonathan Frawley (slides)
- Lecture 10: Software security and AI - exploits, mitigations, and the future. (slides)
Please contact me for permission in using the lab material in other courses:
New 2019! Python- 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)
- 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)
- 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 (this lab involves a 4.7gb virtual machine prepared by Ross Bradley at SRM).
- Answers 1: Sample answers for some of the tasks in practical 1. (pdf)
- Answers 2: Sample answers for some of the tasks in practical 2. (pdf)
- Answers 3: Sample answers for some of the tasks in practical 3. (pdf)
Research Seminars
- Machine learning with problematic datasets in diverse applications (CUHK)
- How to write a good literature review quickly. (PGR seminar)
- Active implicits: segmentation of complex & noisy images. (DBIL seminar)
- GPU-based technology for fast segmentation in 3D imaging data. (DBIL seminar)
- Automated discovery: classification from semi-structured data sources. (Industry seminar)
- Deep Learning: guide for classification, regression, and clustering of big datasets. (Durhack)
- High-level GPGPU programming with OpenCL and CUDA, applied to real-time photorealistic rendering and deformation. (invited seminar, included live-coding session)
- Describing & segmenting images with implicit shape functions - research day (won 2nd best talk).
- Feature-varying skeletonization (CGI).
- Hierarchical unbounded signed-distance fields. (Newcastle University)
- Sparse volumetric deformation: real-time deformation and rendering of massive amounts of volumetric data on current hardware. (PGR seminar)
* seminar slides are available on request.
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