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Neural Networks

External Lecturer: 
Sebastian Goldt
Course Type: 
PhD Course
Academic Year: 
2020-2021
Period: 
January - March
Duration: 
36 h
Description: 

12x2h lectures + 4x3hrs labs = 36 hours (6 weeks: 25/01 - 05/03)

  1. Lab 0 (optional): fundamental programming tools and best practice
  2. Introduction to learning (in high dimensions): Goals of learning; classification vs regression; training vs validation vs testing; linear regression, kernels; fully-connected feedforward networks: representational power; breaking the curse of dimensionality with neural networks?
  3. Lab 1: neural networks from scratch
  4. Computer Vision: analysing spatial correlations using convolutions; (the importance of) datasets (CIFAR10 / 100, ImageNet), basic training algorithm: mini-batch SGD; modern architectures (AlexNet, GoogLeNet, ResNet, DenseNet); acceleration techniques (Nesterov, Adam); dropout, batch normalisation.
  5. Lab 2: computer vision with pyTorch
  6. Machine Learning for the sciences: solving quantum many-body problems with neural networks (case study); guest lectures (TBC)
  7. Robustness in Deep Learning: adversarial examples and defences
  8. Unsupervised learning: GANs and normalising flows; semi-supervised learning.
  9. Recurrent neural networks: Hopfield networks (joint with guest lectures, TBC); vanishing and exploding gradients in recurrent nets; LSTM
  10. Lab 3: Generative models for images
  11. Graph Neural Networks: introduction to GNNs and one application in science, e.g. protein-protein interactions.
  12. Introduction to reinforcement learning
  13. Lab 4: Reinforcement learning
  14. Outlook: From the practice of deep learning to its science; surprises in high dimensions (failure of statistical learning theory bounds), the generalisation puzzle; open problems

Please, notice that this is a course belonging to Data Science Excellence Department programme. MAMA PhD students can plan 33% of their credits (i.e. 50 hrs) from this programme.

Location: 
A-128
Next Lectures: 
Monday, March 8, 2021 - 14:00 to 16:00
Wednesday, March 10, 2021 - 10:00 to 12:00
Tuesday, March 16, 2021 - 09:00 to 12:00
Wednesday, March 17, 2021 - 10:00 to 12:00
Friday, March 19, 2021 - 14:00 to 16:00
Tuesday, March 23, 2021 - 09:00 to 12:00
Wednesday, March 24, 2021 - 10:00 to 12:00
Friday, March 26, 2021 - 14:00 to 16:00

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