Stochastic Processes and Optimization in Machine Learning Lab

This is the repository for the postgraduate course Stochastic Processes & Optimization in Machine Learning. This course is included in the Data Science & Machine Learning (DSML) program of the National Technical University of Athens (NTUA).

Our 2026 course will include the following exercises provided as Jupyter Notebooks:

1. Linear Regression, Polynomial Regression and Logistic Regression
2. K-means Clustering, Principal Component Analysis (PCA) and Autoencoders
3. Markov Chains and Simulation
4. The Metropolis-Hastings Algorithm, Simulated Annealing
5. Restricted Boltzmann Machine (RBM) and Deep Belief Networks
6. Markov Decision Processes and Q-Learning
7. Naive Bayes Classifier (Application in DNS DDoS Cyberattack protection)
8. Radial Basis Function (RBF), Support Vector Machine (SVM) and K-Nearest Neighbors
9. Decision Trees and Random Forests
10. Long Short-Term Memory (LSTM), Explainability (LIME, SHAP)

Note: Some exercises are taken from online sources and the respective code is not developed by us. We try to reference our sources as much as possible within the exercises.