millymobby / ml_labs

This repository contains the laboratory exercises with discussions of the Machine Learning course (2023/24) at the Master's degree in Computer Science at the Sapienza University of Rome

ML Laboratory Repo (Sapienza University of Rome)

This repository contains the laboratory exercises with discussions of the Machine Learning course (2023/24) at the Master's degree in Computer Science at Sapienza University of Rome. For completeness purposes, you'll also find last year's (2022/23) laboratory lessons. This is to show you that the course is in continuous development and evolution.

The coding environment is Google Colab so that students don't have to configure a designated environment with specific Python packages.

The syllabus of the laboratory courses is:

Data pre-processing + Simple ML Models (lab 1)

Data feature pre-processing

Data cleaning - missing data.

Encoding - pitfalls of encoding categorical data, one-hot encodings

Simple ML Models - Decision Trees, Random Forests, XGBoost

XGBoost details - hyperparameters, optimization + overfitting

Naive Bayes, Linear Regression, and SVMs (lab 2)

Naive Bayes, Linear Regression, and SVMs

Bayes classification, Bayes's Theorem

Gaussian and Multinomial Naive Bayes

Simple Linear Regression + Basis Function for nonlinear feature relationships

Ridge and Lasso regularization

Simple insights on uncertainty

Linear vs nonlinear separation hyperplanes

Kernel trick - linear, polynomial, and radial basis function (RBF) kernel

Soft margins of SVMs