Caricamento in corso...

01QWYBH

A.A. 2018/19

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Ict For Smart Societies (Ict Per La Societa' Del Futuro) - Torino

Course structure

Teaching | Hours |
---|---|

Lezioni | 40 |

Esercitazioni in laboratorio | 20 |

Lecturers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|---|---|---|---|---|---|---|

Visintin Monica | Professore Associato | IINF-03/A | 14 | 0 | 17 | 0 | 9 |

Co-lectures

Espandi

Riduci

Riduci

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut |
---|---|---|---|---|---|---|

Pagana Guido | Docente esterno e/o collaboratore | 20 | 0 | 0 | 0 |

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

ING-INF/03 | 6 | B - Caratterizzanti | Ingegneria delle telecomunicazioni |

2018/19

The medium of instruction is English.
The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health".
The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues.
Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.

The medium of instruction is English.
The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health".
The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues.
Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.

Knowledge of:
- basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc)
- e-health and m-health applications
- telemedicine applications
- regression techniques
- clustering techniques
- classification techniques
Ability to:
- understand the issues of a telemedicine application
- apply regression techniques in health problems
- apply clustering techniques in health problems
- apply classification techniques in health problems
- use open-source machine learning software

Knowledge of:
- basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc)
- e-health and m-health applications
- telemedicine applications
- regression techniques
- clustering techniques
- classification techniques
Ability to:
- understand the issues of a telemedicine application
- apply regression techniques in health problems
- apply clustering techniques in health problems
- apply classification techniques in health problems
- use open-source machine learning software

Knowledge of probability theory, linear algebra, optimization techniques

Knowledge of probability theory, linear algebra, optimization techniques

- Description of some e-health, m-health, and telemedicine applications (2 CFU) on the following topics: smart aging, fitness, Parkinson's disease, EEG, ECG, dermatology, lean in health care (2CFU).
- Review of linear algebra and basics on optimization methods.
- Introduction to Python (0.5 CFU).
- regression techniques: linear and nonlinear, Gaussian processes (1CFU)
- clustering techniques: k-means, hierarchical trees (0.5 CFU)
- classification techniques: decision trees and information theory, SVM (very short description), Hidden Markov Models (1.5 CFU)
- Independent component analysis (ICA) applied to EEG (0.5 CFU)

- Description of some e-health, m-health, and telemedicine applications (2 CFU) on the following topics: smart aging, fitness, Parkinson's disease, EEG, ECG, dermatology, lean in health care (2CFU).
- Review of linear algebra and basics on optimization methods.
- Introduction to Python (0.5 CFU).
- regression techniques: linear and nonlinear, Gaussian processes (1CFU)
- clustering techniques: k-means, hierarchical trees (0.5 CFU)
- classification techniques: decision trees and information theory, SVM (very short description), Hidden Markov Models (1.5 CFU)
- Independent component analysis (ICA) applied to EEG (0.5 CFU)

Lectures will describe the health context and the problem to be solved, then the relevant ICT/learning machine methods to be used to solve the problem are discussed and implemented in the laboratory classes. For each lab a report is required.

Lectures will describe the health context and the problem to be solved, then the relevant ICT/learning machine methods to be used to solve the problem are discussed and implemented in the laboratory classes. For each lab a report is required.

- Class slides will be available on the portal
- K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012
- Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006
- David J.C. MacKay, "Information Theory, Inference and Learning Algorithms"
Cambridge University Press 2003
- C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006

- Class slides will be available on the portal
- K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012
- Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006
- David J.C. MacKay, "Information Theory, Inference and Learning Algorithms"
Cambridge University Press 2003
- C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006

...
At the end of each of the four labs the student must write a report; each of these reports gets a grade between 0 and 5 (0 if the report is missing), for a total maximum grade of 20; these 4 reports will be evaluated in the exam session of January-February. For the subsequent exam sessions, the student must write just one report that includes the description of the 4 lab experiences. Together with the report the student must also provide the zipped folder with the Python scripts.
The results described in the report and the used methods will be discussed during the oral exam (half an hour, 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms described in the lectures).
The final grade depends on the correctness of the results, the completeness and clearness of the report (max grade 20), and on the ability to describe and critically discuss the obtained results during the oral exam (max grade 10). The maximum grade of a poorly written report is 16. The "lode" is given to students with an overall grade 30 who showed particular skills in writing the report and/or found brilliant solutions in the lab experiences.
The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.

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.

At the end of each of the four labs the student must write a report; each of these reports gets a grade between 0 and 5 (0 if the report is missing), for a total maximum grade of 20; these 4 reports will be evaluated in the exam session of January-February. For the subsequent exam sessions, the student must write just one report that includes the description of the 4 lab experiences. Together with the report the student must also provide the zipped folder with the Python scripts.
The results described in the report and the used methods will be discussed during the oral exam (half an hour, 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms described in the lectures).
The final grade depends on the correctness of the results, the completeness and clearness of the report (max grade 20), and on the ability to describe and critically discuss the obtained results during the oral exam (max grade 10). The maximum grade of a poorly written report is 16. The "lode" is given to students with an overall grade 30 who showed particular skills in writing the report and/or found brilliant solutions in the lab experiences.
The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.

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.