ECTS - University of Pisa
| Modules | Area | Type | Hours | Teacher(s) | |
| ARTIFICIAL INTELLIGENCE II | INF/01 | LEZIONI | 48 |
|
The course aims to introduce the paradigms to neural networks and deep learning, including the basics of recurrent neural networks and models for complex data, model design and validation, and application to health problems and case studies
The course aims to introduce the paradigms to neural networks and deep learning, including the basics of recurrent neural networks and models for complex data, model design and validation, and application to health problems and case studies
The assessment of knowledge will be the subject of the written and project exam evaluation.
The assessment of knowledge will be the subject of the written and project exam evaluation.
The student who completes the course successfully will be able to Identify problems facing healthcare providers that machine learning can solve and analyze how AI affects patient care safety, quality, and research.
The student who completes the course successfully will be able to Identify problems facing healthcare providers that machine learning can solve and analyze how AI affects patient care safety, quality, and research.
The student will have to solve a deep learning problem during a practical test.
The student will have to solve a deep learning problem during a practical test.
The student will acquire a method to deal with deep learning problems and to select the most effective solution to be adopted
The student will acquire a method to deal with deep learning problems and to select the most effective solution to be adopted
During the lab sessions, the accuracy and precision of the activities carried out will be evaluated
During the lab sessions, the accuracy and precision of the activities carried out will be evaluated
Basic knowledge of mathematics
Knowledge of programming in python
Knowledge of the various machine learning techniques presented in the Artificial Intelligence I course
Lo studente è invitato a verificare l'esistenza di eventuali propedeuticità consultando il Regolamento del Corso di studi relativo al proprio anno di immatricolazione. Un esame sostenuto in violazione delle regole di propedeuticità è nullo (Regolamento didattico d’Ateneo, art. 24, comma 3)" (Regolamento didattico d’Ateneo, art. 24, comma 3)
Basic knowledge of mathematics
Knowledge of programming in python
Syllabus:
Syllabus:
Recommended book: Introduction to Deep Learning for Healthcare, Cao Xiao Jimeng Sun
Papers on different algorithms described during the course
Slides of the lectures
Code written during the exercises
Recommended book: Introduction to Deep Learning for Healthcare, Cao Xiao Jimeng Sun
Papers on different algorithms described during the course
Slides of the lectures
Code written during the exercises
Written test plus individual project and oral exam
Written test plus individual project and oral exam
