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Fundamentals of Artificial Intelligence, Machine and Deep Learning
01VSDWS, 01VSDSM
A.A. 2025/26
Course Language
Inglese
Degree programme(s)
Master of science-level of the Bologna process in Data Science And Engineering - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 85 |
| Esercitazioni in laboratorio | 15 |
| Tutoraggio | 40 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Caputo Barbara | Professore Ordinario | IINF-05/A | 80 | 0 | 8 | 0 | 1 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/05 | 10 | B - Caratterizzanti | Ingegneria informatica |
|---|
2025/26
The course is offered on the II semester of the I year. The course is taught in English. The course addresses the core issues in machine learning, with a special focus on algorithms and theory of statistical machine learning, and modern techniques for deep learning. Lab activities will quip students with first-hand experience on modern optimization methods and programming framework most used in advance research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.
The course is offered on the II semester of the I year. The course is taught in English. The course addresses the core issues in machine learning, with a special focus on algorithms and theory of statistical machine learning, and modern techniques for deep learning. Lab activities and reading groups will quip students with first-hand knowledge and experience on modern optimization methods and programming framework most used in advance research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.
- Knowledge of the main characteristics of artificial Intelligence: historical overview and modern definition; role of Machine and deep learning
- Knowledge of the main characteristics of Statistical Machine Learning: theory and algorithms
- Knowledge of the main characteristics of Artificial Neural Networks
- Knowledge of the main characteristics of modern Deep Learning techniques
- Knowledge of the main characteristics of artificial Intelligence: historical overview and modern definition; role of Machine and deep learning
- Knowledge of the main characteristics of Statistical Machine Learning: theory and algorithms
- Knowledge of the main characteristics of Artificial Neural Networks
- Knowledge of the main characteristics of modern Deep Learning techniques
- Knowledge of the main characteristics of modern Transformers techniques
- Hands-on experience on state of the art deep learning algorithms
Probability
Probability; programming in python
- Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning.
- Overview of fundamental knowledge of probability.
- Generative and discriminative methods.
- Perceptron. - Artificial Neural Networks.
- Support Vector Machines.
- Kernel functions.
- Active learning
- Convolutional Neural Networks.
- Stochastic Gradiant Descent.
- Batch Normalization.
- Generative Adversarial Networks
- Recurrent Neural Networks.
- Learning theory.
- Data bias
- Learning to learn
- Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning.
- Overview of fundamental knowledge of probability.
- Generative and discriminative methods.
- Basics of learning theory
- Perceptron.
- Artificial Neural Networks.
- Support Vector Machines.
- Kernel functions.
- Active learning
- Convolutional Neural Networks.
- Recurrent Neural Networks and Transformers
- Optimization and regularization techniques
- Visualizations
- Generative Adversarial Networks
- Reinforcement learning
The course includes practices on the lecture topics and laboratory sessions during which the students will form team and work on specific projects assigned to them. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.
The course includes practices on the lecture topics and laboratory sessions during which the students will form team and work on specific projects assigned to them. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.
Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching Portal.
Reference books:
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT press.
- K. P. Murphy. Machine Learning: a probabilistic perspective. MIT Press
Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching Portal.
Reference books:
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT press.
- K. P. Murphy. Machine Learning: a probabilistic perspective. MIT Press
Slides; Esercitazioni di laboratorio; Video lezioni dell’anno corrente; Strumenti di collaborazione tra studenti;
Lecture slides; Lab exercises; Video lectures (current year); Student collaboration tools;
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
...
The exam includes a mandatory oral part, and the evaluation of the report on the team projects assigned during the course.
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Group project;
The exam will consist of:
1) Oral questions on the theoretical content of the course
2) Oral questions on the theoretical and methodological content of the group project
3) Oral questions on the implementation and experimental results of the group project
The final grade will be given by an assessment of the three points above plus an assessment of the group work, with each part equally weighted (0-8 points each).
The laude is assigned to students with a total score higher than 30 points.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.