Prerequisites & Preparation

To ensure a productive experience at the 3rd Summer School on Advancing Scientific Discovery with AI, participants are expected to prepare according to their current level of expertise, in order to fulfil the prerequisites before the start of the summer school.

💻 Technical Requirements

Participants must bring their own personal laptop for the hands-on sessions. Please ensure the following environment is installed and functional:

Python Stack: NumPy, Matplotlib, Jupyter Notebook
Machine Learning: Scikit-learn
Deep Learning: PyTorch & PyTorch-Lightning

📚 Recommended Resources

Statistical Learning

Introduction to Statistical Learning (ISLP)
statlearning.com
Elements of Statistical Learning (ESL)
Download PDF

Deep Learning

The Little Book of Deep Learning
fleuret.org/public/lbdl.pdf
Understanding Deep Learning (Simon Prince)
udlbook.github.io

📝 Preliminary Work

Beginners

Lecture #1: Read Chapters 2-4 of ISLP (Linear models, regression & classification).

ISLP (Linear models, regression & classification).

Lecture #2: Read Chapter 14 of Understanding Deep Learning.

Tutorials: Complete the cs231n Python/Numpy tutorial and PyTorch Basics.

Advanced

Lecture #1: Read Chapters 2-4 and 11 of ESL (Regularization, Perceptron, Neural Nets).

Deep Learning: Read Chapters 4-13 of Understanding Deep Learning (CNNs, Transformers, GNNs).

Generative Models: Read Chapters 15, 16 & 18 of Understanding Deep Learning (GANs, VAEs, Diffusion).

Labs: Complete Notebooks for Ch. 1-10 of Understanding Deep Learning.

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