Machine Learning
A.Y. 2025/2026
Learning objectives
The objective of the Machine Learning course is to provide the basic skills to analyze real problems, identify proper solutions for knowledge discovery, and design/implement analytic models. The course presents the best practices from both a theoretical and practical perspective with special focus on Human Centered application problems.
The course covers numerous well-studied methods such as classification, regression, structured prediction, clustering, and representation learning. It familiarizes the students with popular techniques for pattern recognition, knowledge discovery, and data analysis/modeling using Python, the most common language in the field.
The course covers numerous well-studied methods such as classification, regression, structured prediction, clustering, and representation learning. It familiarizes the students with popular techniques for pattern recognition, knowledge discovery, and data analysis/modeling using Python, the most common language in the field.
Expected learning outcomes
At the end of the course students will be able to understand and discuss the principles of machine learning. They will be able to analyze practical problems, and to design and implement suitable solutions. They will be familiar with the most important techniques in the field and will be able to use them to build effective machine learning systems.
The students will be able to:
- Understand the broad applications of machine learning across various societal contexts
- Develop a comprehensive understanding of ML concepts and identify the best models to fit various applications
- Integrate multiple data management techniques: data preprocessing, learning, regularization, and model selection
- Develop and implement machine learning algorithms and devise solutions to real-life problems within human centric domains
- Describe the properties of models and algorithms for learning and explain the practical implications of the results
- Collaborate effectively on machine learning projects and assignments with both experts and peers.
The students will be able to:
- Understand the broad applications of machine learning across various societal contexts
- Develop a comprehensive understanding of ML concepts and identify the best models to fit various applications
- Integrate multiple data management techniques: data preprocessing, learning, regularization, and model selection
- Develop and implement machine learning algorithms and devise solutions to real-life problems within human centric domains
- Describe the properties of models and algorithms for learning and explain the practical implications of the results
- Collaborate effectively on machine learning projects and assignments with both experts and peers.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
FUNDAMENTALS
- Introduction to Machine Learning, numpy and pandas with examples
- Plotting with matplotlib and seaborn with examples
- Elements of probability and statistics
- Optimization techniques for ML
MACHINE LEARNING
- Statistical learning, supervised (regression, classification) and unsupervised (clustering)
SUPERVISED LEARNING
- Data preparation with examples
- Feature selection and dimensionality reduction with examples
- Performance evaluation
- Model selection: k-fold cross validation, leave one out, batches or mini-batches
- Ensemble models
SUPERVISED LEARNING: REGRESSION
- Introduction to linear models, KNN, tree, SVR, random forest, boosting
SUPERVISED LEARNING: CLASSIFICATION
- Introduction to logistic regression, KNN, tree, SVC, random forest, boosting
UNSUPERVISED LEARNING: CLUSTERING
- Introduction to clustering with exams
ADVANCED
- Feed-forward neural networks, deep learning, convolutional neural networks, generative AI
- Introduction to Machine Learning, numpy and pandas with examples
- Plotting with matplotlib and seaborn with examples
- Elements of probability and statistics
- Optimization techniques for ML
MACHINE LEARNING
- Statistical learning, supervised (regression, classification) and unsupervised (clustering)
SUPERVISED LEARNING
- Data preparation with examples
- Feature selection and dimensionality reduction with examples
- Performance evaluation
- Model selection: k-fold cross validation, leave one out, batches or mini-batches
- Ensemble models
SUPERVISED LEARNING: REGRESSION
- Introduction to linear models, KNN, tree, SVR, random forest, boosting
SUPERVISED LEARNING: CLASSIFICATION
- Introduction to logistic regression, KNN, tree, SVC, random forest, boosting
UNSUPERVISED LEARNING: CLUSTERING
- Introduction to clustering with exams
ADVANCED
- Feed-forward neural networks, deep learning, convolutional neural networks, generative AI
Prerequisites for admission
Knowledge on programming is suggested
Teaching methods
The course is structured in lectures of theory and exercises.
Teaching Resources
Python Data Science Handbook by Jake VanderPlas - Publisher(s): O'Reilly Media, Inc. - ISBN: 978-1-491-91205-8
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman - Springer - ISBN: 978-0-387-84858-7
Human-Centered Artificial Intelligence by Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra - Springer - ISBN: 978-3-031-24349-3
Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong - Cambridge University Press - ISBN: 978-1-108-45514-5
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman - Springer - ISBN: 978-0-387-84858-7
Human-Centered Artificial Intelligence by Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra - Springer - ISBN: 978-3-031-24349-3
Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong - Cambridge University Press - ISBN: 978-1-108-45514-5
Assessment methods and Criteria
The exam consists of a project on a specific topic chosen by the student and an oral exam. The oral exam will include some questions related to the program and a presentation of the project.
Professor(s)