• Fundamentals of statistics and probability (6h) – Lessons
- Basic concepts of statistics and combinatorics
- Basic concepts of probability
- Marginal, joint and conditioned probability and events independence
- Bayes Theorem and Total Probability Theorem
- Composed and iterated experiments

• Random variables (10h): – Lessons + Matlab laboratory
- Introduction to random variables
- Continuous and discrete variables
- Distribution and probability density/mass functions
- Trasformations of random variables
- Characteristic parameters of random variables
- Systems of random variables
- Characteristic parameters of random variables systems

• Introduction to artificial intelligence (2h): – Lessons + Seminars
- Artificial intelligence definition/s and algorithm types
- Artificial intelligence history, from the first inventions to the state-of-the-art
- Uses, benefits, criticalities and issues of artificial intelligence
- Machine learning techniques (16h): – Lessons + Matlab and Python laboratory + Seminars
- Introduction, inference, features reduction, training and validation
- PCA
- K-Means
- Naïve Bayes classifier
- Lineare and logistic regression
- k-NN classifier
- SVM
- Decision/regression tree and random forest
- Main aspects of reinforcement learning

• Fundamentals of neural networks and deep learning (14h): – Lessons + Python laboratories + Seminars
- Introduction to neural networks
- Dimensioning neural networks and hyperparameters
- Improving performances of neural networks
- CNN

- "Teoria della probabilità e variabili aleatorie, con applicazioni all’ingegneria e alle scienze", di A. Bononi e G. Ferrari, ed. Esculapio, 2008.
- "Algoritmi per I’intelligenza artificiale”, di R. Marmo, ed. Hoepli, 2020.
- "Artificial intelligence: a modern approach" 4th ed., di S. Russel, P. Norvig, ed. Global Edition.
- “Hands-on machine learning with Scikit-learn, Keras & TensorFlow” 2nd ed., di A. Géron, O’Reilly ed.
- “Neural Networks and Deep Learning: a Textbook”, di C.C. Aggarwal, ed. Springer.