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PORTALE DELLA DIDATTICA

Machine learning in applications

01URXOV

A.A. 2021/22

Course Language

Inglese

Course degree

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

Course structure
Teaching Hours
Lezioni 30
Esercitazioni in laboratorio 30
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Di Cataldo Santa   Professore Associato ING-INF/05 20 0 0 0 2
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 6 B - Caratterizzanti Ingegneria informatica
2021/22
The unprecedented explosion of ICT and Artificial Intelligence technologies is driving a revolution that is transforming many important sectors of our society, including medicine and industry. Nonetheless, real-world applications, especially in such critical domains, often pose unique challenges and issues that need to be addressed. This course aims to fill the gap between ideal and real and to provide a hands-on experience of the latest solutions and domain-specific applications of ML and DL in biology and industry. In the end, students will learn how to apply the ML and DL models they have studied to real-world applications, how to adapt and extend such models to address domain-specific issues and how to design and build ML and DL applications that are usable, useful, and used.
The unprecedented explosion of ICT and Artificial Intelligence technologies is driving a revolution that is transforming many important sectors of our society, including medicine and industry. Nonetheless, real-world applications, especially in such critical domains, often pose unique challenges and issues that need to be addressed. This course aims to fill the gap between ideal and real and to provide a hands-on experience of the latest solutions and domain-specific applications of ML and DL in biology and industry. In the end, students will learn how to apply the ML and DL models they have studied to real-world applications, how to adapt and extend such models to address domain-specific issues and how to design and build ML and DL applications that are usable, useful, and used.
Knowledge: - State of the art ML and DL model and tools in different application domains Skills: - Developing working ML and DL solutions for complex real-world applications - Experience joint development of projects in a group of engineers
Knowledge: - state of the art ML and DL model and tools in different application domains Skills: - developing working ML and DL solutions for complex real-world applications in biology and industry - experience joint development of projects in a group of engineers
The students are expected to already have basic knowledge of - probability and statistics, linear algebra, calculus - object-oriented programming (Python) - shallow and deep machine learning models and programming frameworks
The students are expected to already have - basic knowledge of probability and statistics, linear algebra, calculus - basic knowledge of object-oriented programming (Python) - basic knowledge of shallow and deep machine learning models and programming frameworks
ML framework in real-world applications: from datum to knowledge [15h] - Overview of ML taxonomy and applications, data types and issues - From sensor to ML-ready data - Data preprocessing and balancing - Feature extraction and selection - From classic ML to DL: recaps of domain-specific models and tools - ML and DL for image analysis and understanding - ML and DL for sequence analysis - Issues and challenges in real-world applications Encompassing the issues of “classic” DL [15h] - Uncertainty modelling and data cleaning - Generative modelling for data-critical applications - Self-supervised and semi-supervised learning - Incremental learning - Explainable models Application domains and hands-on case-studies [30h] - ML for Biology and Medicine - Biological image analysis - Genetic sequence analysis - ML for Industry - Anomaly detection, predictive maintenance, production and logistics optimization - Additive Manufacturing: quality assurance and process optimization
ML framework in real-world applications: from datum to knowledge [15h] - Overview of ML taxonomy and applications, data types and issues - From sensor to ML-ready data - Data preprocessing and balancing - Feature extraction and selection - From classic ML to DL: recaps of domain-specific models and tools - ML and DL for image analysis and understanding - ML and DL for sequence analysis - Issues and challenges in real-world applications Encompassing the issues of “classic” DL [15h] - Uncertainty modelling and data cleaning - Generative modelling for data-critical applications - Self-supervised and semi-supervised learning - Incremental learning - Explainable models Application domains and hands-on case-studies [30h] - ML for Biology and Medicine - Biological image analysis - Genetic sequence analysis - ML for Industry - Anomaly detection, predictive maintenance, production and logistics optimization - Additive Manufacturing: quality assurance and process optimization
The course consists of 30h of frontal lectures and 30h of laboratories. The so-called “laboratories” include - practical exercises on the topics covered by the lectures - project-related activities on several case-studies from biology and industry, that will be conducted in groups with the continuous support of the teachers.
The course consists of 30h of frontal lectures and 30h of laboratories. The so-called “laboratories” include - practical exercises on the topics covered by the lectures - project-related activities on several case-studies from biology and industry, that will be conducted in groups with the continuous support of the teachers.
There is no specific reference text book. Reading material will be made available through portale della didattica. It will include: - slides used during the lectures - research papers about the topics covered during the course - occasionally, pointers to videos/online tutorials
There is no specific reference text book. Reading material will be made available through portale della didattica. It will include: - slides used during the lectures - research papers about the topics covered during the course - occasionally, pointers to videos/online tutorials
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Individual project; Group project;
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; Individual project; Group project;
The exam consists of a group (2-3 people) project, developed during the course: either the extension of one of the case-studies addressed during the laboratories, or a new one agreed-upon with the teacher. Each group will be asked to submit the developed code as well as a report (8-10 pages in double-column format) with a research paper-like description of their work, including: challenges and motivations, state of the art, proposed solution and discussion of the obtained results. The project will be then presented and discussed by all the group’s components through an oral session (either in person or via web-conferencing). After the ppt group presentation (about 15 minutes), each group member will be asked questions about the project and her/his individual contribution to the group activity, as well as general questions on the topics covered by the course. The final grade of each student will consist of: - evaluation of the group project (same for all the group members, 70%). This evaluation takes into account: the complexity of the addressed problem; the originality and richness of the proposed solution; the methodological and technical correctness of the solution; the completeness and quality of the report; the completeness and quality of the oral presentation - evaluation of the individual oral discussion (30%). This evaluation takes into account: the quality of the individual presentation; the individual effort and contribution to the group activity; the correctness of the answers to theoretical and technical questions; the individual communication skills.
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.
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Individual project; Group project;
The exam consists of a group (2-3 people) project, developed during the course: either the extension of one of the case-studies addressed during the laboratories, or a new one agreed-upon with the teacher. Each group will be asked to submit the developed code as well as a report (8-10 pages in double-column format) with a research paper-like description of their work, including: challenges and motivations, state of the art, proposed solution and discussion of the obtained results. The project will be then presented and discussed by all the group’s components through an oral session (via web-conferencing). After the ppt group presentation (about 15 minutes), each group member will be asked questions about the project and her/his individual contribution to the group activity, as well as general questions on the topics covered by the course. The final grade of each student will consist of: - evaluation of the group project (same for all the group members, 70%). This evaluation takes into account: the complexity of the addressed problem; the originality and richness of the proposed solution; the methodological and technical correctness of the solution; the completeness and quality of the report; the completeness and quality of the oral presentation - evaluation of the individual oral discussion (30%). This evaluation takes into account: the quality of the individual presentation; the individual effort and contribution to the group activity; the correctness of the answers to theoretical and technical questions; the individual communication skills.
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Individual project; Group project;
The exam consists of a group (2-3 people) project, developed during the course: either the extension of one of the case-studies addressed during the laboratories, or a new one agreed-upon with the teacher. Each group will be asked to submit the developed code as well as a report (8-10 pages in double-column format) with a research paper-like description of their work, including: challenges and motivations, state of the art, proposed solution and discussion of the obtained results. The project will be then presented and discussed by all the group’s components through an oral session (either in person or via web-conferencing). After the ppt group presentation (about 15 minutes), each group member will be asked questions about the project and her/his individual contribution to the group activity, as well as general questions on the topics covered by the course. The final grade of each student will consist of: - evaluation of the group project (same for all the group members, 70%). This evaluation takes into account: the complexity of the addressed problem; the originality and richness of the proposed solution; the methodological and technical correctness of the solution; the completeness and quality of the report; the completeness and quality of the oral presentation - evaluation of the individual oral discussion (30%). This evaluation takes into account: the quality of the individual presentation; the individual effort and contribution to the group activity; the correctness of the answers to theoretical and technical questions; the individual communication skills.
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