This course aims at giving a broad overview of Machine Learning and Deep Learning methodologies for time-series analysis, with a major focus on Neural Network methods. The course will feature both lectures and hands-on labs.

Topics covered:

• A gentle introduction to Machine/Deep Learning
• Deep Learning libraries: TensorFlow, Keras (+sklearn)
• Recurrent Neural Networks: basics and advances
  • Vanilla RNN
  • Gated architectures: LSTMs, GRUs
  • Bi-directional RNNs
  • Deep Recurrent Neural Networks
  • (Deep) Reservoir Computing
• Convolutional Neural Networks for time-series
• Attention Mechanisms, Transformers
• Brief notes on continuous depth models, Neural ODEs