This repository contains the didactic material illustrated during the lesson of:
- PhD @ IMT School for advanced studies Lucca
- Master II Livello - Data Science and Statistical Learning (MD2SL)
- Introduction to Python for data science
- Introduction to pandas and jupyter
- Dataframes, manipulation, and typical operations
- Reading from files
- Plotting libraries
- Unsupervised learning (some example)
- Introduction to unsupervised learning
- Examples with k-means
- Examples with hierarchical clustering
- Examples with density based clustering
- Dimensionality reduction
- Introduction
- Principal component analysis (PCA)
- t-distributed stochastic neighbor embedding (t-SNE)
- Supervised learning
- Scikit-learn for classification/regression
- Pipeline and data handling
- Random Forest / XGBoost / SVM / etc.
- Classification evaluation
- Neural networks and deep learning
- Introduction and recap
- Implementation in Python
- Deep learning:
- Introduction to Pytorch
- RNN
- CNN
- Advanced on Deep Learning
- Attention Mechanism
- VAE
- Transformer