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Docente
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RODOLA' EMANUELE
(programma)
- Data, features, and embeddings
- Data awareness
- Modeling prior knowledge
- The curse of dimensionality
- Task-driven features and invariances
- Recap of linear algebra
- Vector spaces, bases
- Linear maps
- Matrix notation and matrix algebra
- Tensors and tensor operations
- Parametric models and regression
- Linear and polynomial regression
- Convexity and Lp norms
- Underfitting and overfitting
- Cross validation
- Logistic regression
- Optimization
- Gradient descent
- Stochastic gradient descent
- Learning rate, decay, momentum, batch size
- Forward and reverse-mode automatic differentiation
- Deep neural networks
- Multi layer perceptron
- Backpropagation
- Universal approximation theorems
- Autograd and modules
- Invariance, equivariance, compositionality
- Convolutional neural networks
- Pooling
- Double descent
- Regularization: weight penalty, early stopping, dropout, batchnorm
- Generative models
- PCA
- Manifolds and the manifold hypothesis
- Representation learning
- Autoencoders: variational, contractive, denoising
- Generative adversarial networks
- Adversarial learning
- Decision boundaries
- Black-box and white-box attacks
- Adversarial perturbations: universal and one-pixel
- Adversarial training
- Geometric deep learning
- Learning on graphs and point clouds
- Learning on surfaces
- Generative models of structured data
- Adversarial surfaces
 Data la natura altamente dinamica dell'area coperta da questo corso avanzato, non è previsto un testo unico di riferimento. Durante il corso verranno indicate e fornite di volta in volta le fonti sotto forma di articoli scientifici e capitoli di libri.
Come riferimento generale, i seguenti libri possono rivelarsi utili:
Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
MIT Press, 2016
Deep Learning with PyTorch
Vishnu Subramanian
Packt, 2018
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