StochasticsLabPublic

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 2022 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), Self-Organized Maps (SOM) and Autoencoders
3. Markov Chains and Simulation (heavily based on the Stochastic Processes course of the 6th semester in ECE NTUA)
4. The Metropolis-Hastings Algorithm
5. Simulated Annealing
6. Restricted Boltzmann Machine (RBM) and Deep Belief Networks
7. Markov Decision Processes and Q-Learning
8. Bellman-Ford Algorithm (Application in the BGP protocol)
9. Naive Bayes Classifier (Application in DNS DDoS attack mitigation: the DNS Water Torture Attack use case)
10. Radial Basis Function (RBF), Support Vector Machine (SVM) and K-Nearest Neighbors
11. Decision Trees and Random Forests
12. Hopfield Networks, Long Short-Term Memory (LSTM) (Application in Network Security)

Note: Some exercises are taken from online sources.