Machine Learning
A.Y. 2023/2024
Learning objectives
The course introduces the principles of machine learning
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 a problem, and to design and implement a solution. They will be familiar with the most important techniques in the field and will be able to use them to build machine learning systems.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Basic concepts:
- introduction to Python language
- elements of probability and statistics
- classes of optimization problems
- gradient descent, regularization
- machine learning: statistical learning , regression, classification, clustering, generalization
- training approaches: k-fold cross validation, leave one out, batches or mini-batches, model selection
- data preparation
- bayesian decision theory
- regression models
- feed-forward neural networks
- linear and non-linear Support Vector Machines
- multi-class models
- generative models: autoencoders and GANs
- deep learning and convolutional neural networks
- recurrent neural networks
- genetic algorithms
- introduction to Python language
- elements of probability and statistics
- classes of optimization problems
- gradient descent, regularization
- machine learning: statistical learning , regression, classification, clustering, generalization
- training approaches: k-fold cross validation, leave one out, batches or mini-batches, model selection
- data preparation
- bayesian decision theory
- regression models
- feed-forward neural networks
- linear and non-linear Support Vector Machines
- multi-class models
- generative models: autoencoders and GANs
- deep learning and convolutional neural networks
- recurrent neural networks
- genetic algorithms
Prerequisites for admission
Calculus, Elements of Python and Object Oriented programming.
Teaching methods
Frontal lessons with working examples and demos in jupyter notebooks - Laboratory tasks - Materials by the teachers
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
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
Assessment methods and Criteria
Oral exam with discussion on theoretical concepts and the machine learning practical examples presented in the course
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professors:
Cabri Alberto, Soto Gomez Mauricio Abel
Educational website(s)
Professor(s)