Section outline
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The module covers some recent and interesting development and research topics in the field of machine learning. Topics choice is likely to vary at each edition. Example topics include: deep learning for graphs, continual learning, distributed learning, learning-reasoning integration, edgeAI, lerning beyond backpropagation, neural networks inspired by dynamical systems, ... The module concludes with a final lecture which discusses the course content retrospectively and details the exam modalities, topics and deadlines.
Date Topic References Additional Material 34 14/05/2025
(16-18)Deep learning for graphs I: Fundamentals
learning with structured data, learning tasks on graphs, message-passing architectures, survey of foundational models for graphs[SD] Chapter 13 Software
- PyDGN: our in-house DLG library
- PyTorch geometric
- Deep graph library
Additional readings
[57-58] Seminal works on neural networks for graphs
[59] Recent tutorial paper35 15/05/2025
(14-16)Deep learning for graphs II: advanced topics
graph convolutional networks, graph pooling, generative learning on graphs, probabilistic graph models, non-dissipative graph message passing, neural algorithmic reasoning[SD] Chapter 13 Additional readings
[60] A work on generalizing pooling to graphs
[61] Probabilistic learning on graphs
[62] Non-dissipative message passing via neural graph ODEs
[63] Survey on deep learning for dynamic graphs
[64] Neural algorithmic reasoning following duality structure in optimization problems[65] Seminal work on graph transformers
20/05/2025 NO LECTURE (due to Giro d'Italia closures) 36 21/05/2025
(16-18)(Deep) Reinforcement Learning fundamentals [SD] Sections 19.1-19.3.1, 19.4, 19.5 (no derivation of policy gradient) Additional readings
[66] Original Q-Learning algorithm
[67] Original DQN paper
[68] Learning with the actor-critic architecture
[69] A masterpiece paper deriving trust-region policy optimization (technical by worth the read)37 22/05/2025
(14-16)Final lecture