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:
- Linear Regression, Polynomial Regression and Logistic Regression
- K-means Clustering, Principal Component Analysis (PCA), Self-Organized Maps (SOM) and Autoencoders
- Markov Chains and Simulation (heavily based on the Stochastic Processes course of the 6th semester in ECE NTUA)
- The Metropolis-Hastings Algorithm
- Simulated Annealing
- Restricted Boltzmann Machine (RBM) and Deep Belief Networks
- Markov Decision Processes and Q-Learning
- Bellman-Ford Algorithm (Application in the BGP protocol)
- Naive Bayes Classifier (Application in DNS DDoS attack mitigation: the DNS Water Torture Attack use case)
- Radial Basis Function (RBF), Support Vector Machine (SVM) and K-Nearest Neighbors
- Decision Trees and Random Forests
- Hopfield Networks, Long Short-Term Memory (LSTM) (Application in Network Security)
Note: Some exercises are taken from online sources.