Schedule

Note: This is an indicative schedule for the course, it will be updated with a more detailed version soon

Monday:

Morning:  

• • 9:00 - 9:30: Lecture: Introduction to Course/overview
  • 9:30 - 10.00: Exercise: Introduction to Jupyter Notebooks
  • 10:00 - 12:00: Lecture: Basic theory of Feed-forward Neural Networks

Afternoon:

• • 13:00 - 16:00: Lecture: Introduction to Keras and TensorFlow part 1 
  • 16:00 - 17:00: Lab: Training Neural Networks in Keras 

Tuesday: 

Morning:

• • 9:00 - 9:30: Recap of previous day
  • 9:30 - 12:00: Lecture + lab:  Introduction to Keras/TensorFlow part 2

Afternoon:

• • 13:00 - 17:00: Lecture + Lab: Convolutional Neural Networks 

Wednesday: 

Morning:

• • 9:00 - 9:30: Recap of previous day
  • 9:30 - 11:00: Lecture:  Best practices for NN design, part 1
  • 11:00 - 12:00: Lab: Rigorous splitting of training, validation and test sets

Afternoon:

• • 13:00 -13:50: Lecture: Unsupervised learning and Neural Networks, autoencoders.
  • 14:05 - 17:00: Exercises. Links to an external site.

Thursday: 

Morning:

• • 9:00 - 9:30: Recap of previous day
  • 9:30 - 10:00: Lecture:  Best practices for NN design, part 2
  • 10:00 - 12:00: Lab: Target leakage

Afternoon:

• • 13:00 - 17:00 Lecture + Lab: Recurrent Neural Networks
  • Lecture: Introduction to Recurrent Neural Networks

Friday: 

Morning:

• • 9:00 - 12.00: Lecture: Graph Neural Networks, Transformers

            Afternoon:

• • 13:30 - 15:30: Lecture:  Best practices for NN design, part 3
  • 15:30 - 17:00: [Open question time] How to apply Neural Networks to your own projects?