PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

Elenco notifiche



Advanced Machine Learning

01URWOV

A.A. 2025/26

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino

Course structure
Teaching Hours
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Co-lectures
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 C - Affini o integrative Attività formative affini o integrative
2024/25
The course, optional for the Master degree in computer engineering, is offered in the I semester of the II year. The course is taught in English. The course addresses the core issues in modern Artificial intelligence and machine learning, with a special focus on advanced algorithms and theory of shallow and deep machine learning with applications in Computer Vision and Natural Language Processing. Lab activities will equip students with first-hand experience on modern optimization methods and programming framework most used in advanced research and companies as of today, and to have first hand experiences on the properties of such algorithms on specific case studies.
The course, optional for the Master degree in computer engineering, is offered in the I semester of the II year. The course is taught in English. The course addresses the core issues in modern Artificial intelligence and Machine Learning: it covers the basic components of convolutional neural networks architectures and presents the evolution of deep learning models with an overview of the most relevant literature, finally focusing on advanced deep learning algorithms with Computer Vision and Natural Language Processing applications. Students will learn what are the main properties of such techniques and how to design tailored approaches for specific case studies. Lab activities will equip students with first-hand experience in modern optimization methods and programming frameworks most used in advanced research and companies.
- Knowledge of the main characteristics of artificial intelligence: historical overview and modern definition - Knowledge of how to formalize a learning problem from the mathematical foundations of the optimization objectives to the composition of a deep architecture - Knowledge of the main characteristics of modern deep learning techniques with practical engineering tricks for end-to-end training and fine-tuning the networks - Basic knowledge about how to deal with research questions, how to organize a project and prepare a scientific conference report
- Knowledge of the main characteristics of artificial intelligence: historical overview and modern definition - Knowledge of how to formalize a learning problem from the mathematical foundations of the optimization objectives to the composition of a deep architecture - Knowledge of the main characteristics of modern deep learning techniques with practical engineering tricks for end-to-end training and fine-tuning the networks - Basic knowledge about how to deal with research questions, how to organize a project and prepare a scientific conference report
- Linear Algebra - Probability theory concepts - Basic concepts of decision theory (model optimization) - Python: Basic elements
- Linear Algebra - Probability theory concepts - Basic concepts of decision theory (model optimization) - Python: Basic elements
- Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning - Overview of fundamental knowledge of probability - Overview of decision theory: loss, risk, Probably Approximately Correct (PAC) Learning - Perceptron and Support Vector Machines beyond classification - Artificial Neural Networks - Convolutional Neural Networks basic algorithms: Backpropagation and Stochastic Gradient Descent - Training a CNN (data preprocessing, weight initialization and hyperparameter optimization) - Visualizing and Understanding the CNN inner working - Multi-Task and Structured Output Learning (Semantic Segmentation and Detection) - Unsupervised and Metric Learning (Siamese networks and Contrastive Learning for Retrieval) - Generative Networks (Autoencoders and GANs) - Recurrent Neural Networks - Learning with few samples and across domains - Active and Incremental learning - Basic concepts and networks for Reinforcement Learning Besides the lectures, the course will include selected topic-dedicated reading group sessions where the students will actively participate by presenting and discussing recent research papers.
Part 1 (12h) - Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning - Overview of fundamental knowledge of probability - Overview of decision theory: loss, risk, Probably Approximately Correct (PAC) Learning - Perceptron and Artificial Neural Networks Part 2 (12h) - Introduction to Pytorch and useful deep learning libraries - Convolutional Neural Networks basic algorithms: Backpropagation and Stochastic Gradient Descent - CNN architectures overview (from Alexnet to ResNet and Transformers) - Training a CNN (data preprocessing, weight initialization and hyperparameter optimization) - Visualizing and Understanding the CNN inner working + 6h exercise sessions on CNN for single and multi-modal object classification Part 3 (12h) - Multi-Task and Structured Output Learning (Semantic Segmentation and Detection) - Unsupervised and Metric Learning (Siamese networks and Contrastive Learning for Retrieval) - Generative Networks (Autoencoders and GANs) - Recurrent Neural Networks and Autoregressive approaches - Learning with few samples and across domains + 6h exercise sessions on action recognition, adversarial learning and domain adaptation Part 4 (12h) Reading group and discussion sessions on selected topics. The students will actively participate by presenting recent research papers.
- 48 hours of lectures (theory section) - 12 hours of 'laboratory' exercises (practical section)
- 48 hours of lectures (theory section) - 12 hours of 'laboratory' exercises (practical section)
Slides + State of the art deep learning top conference and journal papers. Optional: specific pointers to chapters of the following books will be indicated during the lectures. Artificial Intelligence: A Modern Approach S. Russell, P.Norvig Deep Learning by I. Goodfellow, Y. Bengio, A. Courville. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Machine Learning: a Probabilistic Perspective by Kevin P. Murphy The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman Pattern Recognition and Machine Learning, Christopher M. Bishop Machine Learning, Tom M. Mitchell A Course in Machine Learning, Hal Daumé III Neural Networks - a Systematic Introduction, Raul Rojas
Slides + State of the art deep learning top conference and journal papers. Optional: specific pointers to chapters of the following books will be indicated during the lectures. Artificial Intelligence: A Modern Approach S. Russell, P.Norvig Deep Learning by I. Goodfellow, Y. Bengio, A. Courville. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Machine Learning: a Probabilistic Perspective by Kevin P. Murphy The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman Pattern Recognition and Machine Learning, Christopher M. Bishop Machine Learning, Tom M. Mitchell A Course in Machine Learning, Hal Daumé III Neural Networks - a Systematic Introduction, Raul Rojas
Video lezioni dell’anno corrente;
Video lectures (current year);
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
... - paper presentation during the course: the students are asked to read, present and discuss a recent research paper - final group (3 students) project. It consists in reproducing and extending a current state of the art deep learning method. The students will elaborate a pdf report of about 8 pages in ieee paper format and will present the work in a 15 minutes talk during the oral exam. - oral exam on project presentation (group) and on the theory topics presented during lectures (individual questions).
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;
- (30%) Paper presentation during the course: the students are asked to read, present, and discuss a recent research paper. The goal of this part of the exam is to evaluate the students' comprehension of a scientific research text, by identifying: (a) what is the task that the authors are studying; (b) how the newly proposed approach relates to previous work; (c) the intuition at the basis of the proposed algorithm; (d) the experimental setting and metrics; (e) discuss the results and explain potential limitations. The presentation is usually performed in groups (3 students), followed by individual questions. - (40%) Project Work. It consists in reproducing and extending a current state-of-the-art deep learning method. The students can choose among a set of topics and are asked to re-implement an existing approach, run experiments, and test some suggested extensions. The students will work in groups and submit 10 days before the oral exam a PDF report of 8 pages in IEEE paper format. This part of the exam aims at evaluating the students' ability in coding and experimentally validating a deep learning model as well as reporting and discussing the obtained results. The evaluation process includes a check on the code and a review of the submitted pdf report. Extra points (for honors, "lode") are assigned to original, well-justified, and experimentally validated method extensions proposed by the students. - (30%) Oral Presentation and Theory questions. This part of the exam aims at verifying the equal contribution of all the authors to the project work. The students are asked to present the work done. The content and timing of the presentation will be evaluated as well as the answers to individual questions. The students are also asked about more general theory topics presented in class.
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.
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