Course Resources
These references are optional unless a schedule entry or assessment guideline says otherwise.
Textbooks
- DL: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Free online version
- PML1: Probabilistic Machine Learning: An Introduction by Kevin Murphy. Free online version
- PML2: Probabilistic Machine Learning: Advanced Topics by Kevin Murphy. Free online version
- PRML: Pattern Recognition and Machine Learning by Christopher Bishop. Free online version
- GNN: Graph Neural Networks: Foundations, Frontiers, and Applications by Lingfei Wu, et al. Free online version
- GRL: Graph Representation Learning by William Hamilton. Free online version
- SDE: Brownian Motion and Stochastic Calculus by Ioannis Karatzas and Steven Shreve, 2012.
Related Courses
- African Master’s in Machine Intelligence (AMMI 2021): Geometric Deep Learning
- Stanford CS224W, Winter 2021: Machine Learning with Graphs
- McGill COMP 766, Winter 2020: Graph Representation Learning
- University of Toronto STA 4273 / CSC 2547, Spring 2018: Learning Discrete Latent Structure
Optional Background and Further Reading
These papers complement the revised 20-paper presentation schedule; they do not have separate presentation slots.
- PointNet++: background for point-cloud models before Point Transformer V3.
- G-CNNs: foundational group convolution, covered in the instructor lecture.
- KTO: an alternative preference-learning objective to compare with DPO.
- DAPO: additional RL training techniques to compare with DeepSeek-V3.2.
- Understanding Reasoning from Pretraining to Post-Training: further reading alongside the reasoning and RL presentations.
Paper Lists
- Must-read papers on graph neural networks
- Awesome Graph Neural Networks
- Awesome Equivariant Networks
- Awesome LLMs
- Awesome Diffusion Models
- Awesome Energy-Based Models
- Generative Models on Papers with Code
Sample Project Reports
The report titles are retained as historical examples; the submitted files are archived privately.
2022 Winter Term 1
- Node-element Hypergraph Message Passing for Mesh-based Simulations
- Benchmarking Attention-based Quantum State Tomography
- Heram: Multi-Magnification Graph-Structured Whole Slide Image Representation
- Vision Transformers for Classification in Small-Sized Chest X-Ray Datasets
- DiffuseDRAW: Structured Latent Variables Model with Discrete Diffusion Prior
- Graph-Guided Unsupervised Clustering for Source-Free Domain Adaptation
- EchoGNN with Contrastive Learning
- Deep Learning-Based Calibration for Millimeter-Wave Phased-Array Antennas
2021 Winter Term 2
- Physics Aware Joint Inference for the Cryo-EM Inverse Problem: Normal Modes, Global 3D Pose and CTF Defocus
- V-DMGNN-GAN: Spatial Inpainting for Human Motion Prediction
- Improving Out-Of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
- 3D Ultrasound Segmentation using Transformers
- Graph-enhanced Transformers for Referring Expressions Comprehension
- HiGNN: Hierarchical Left Ventricle Landmark Detection with Graph Neural Networks
- Towards Domain Generalized Segmentation with Transformer
- A Transformer-based Video Analysis Framework for Estimating Ejection Fraction from Echocardiograms