This repository contains material for introducing common approaches in the machine learning pipeline. It consists of several jupyter notebooks for various tasks and introduces privacy-preserving machine learning using encrypted inference. Finally, the concept of Membership Inference Attack (MIA), which can lead to a serious privacy leakage is introduced.

This repository is made by the Euclid team, Democritus University of Thrace, Dept. of Electrical & Computer Engineering.

• Exploratory Data Analysis: Summarize the main characteristics of a dataset.
• Feature Engineering: Select, transform and generate new features on a dataset.
• Standardization and Normalization: Scale variables.
• Imputation: Handle NaN/null values.
• Feature Selection: Reduce the number of input variables.
• UnderSampling and OverSampling: Handle imbalanced datasets.
• Deep Learning with PyTorch: Build deep neural networks with PyTorch.
• Cross Validation: Evaluate the generalization of a machine learning model.
• Tuning with Grid-search: Find optimal parameters on a given model.
• Tuning with Bayesian Optimization: Find optimal parameters on a given model using Bayesian optimization.
• Introduction to Regression: Build regressor models.
• Introduction to Community Detection on Graphs: Find cluster in graph-based representations.
• Dimensionality Reduction: Project or transform data into a low-dimensional space.
• Introducing CryptoNets: Evaluate a model on encrypted data.
• Introducing MIA: Identify the training set that used to generate a predictive model.

We recommend the configuration using Conda and Python 3.8+ or using an external jupyter environment such as Colab or Kaggle.

• imbalanced_learn
• matplotlib
• numpy
• pandas
• scikit_learn
• torch
• optuna
• seaborn
• tenseal
• networkx
• python-louvain
• notebook

pip install -r requirements.txt