At the end of the course, the student masters the basic algorithms, tools and systems for the management, processing and analysis of dataset. The student is able to design and develop simple systems oriented to medical and biomolecular real-world problems. Principles of generative AI will also be part of the learning objectives, as well as their biomedical applications.

00. Course Introduction

01. Deep Learning
    01.01 Deep Learning Introduction
    01.02 Convolutional Neural Network
    01.03 Recurrent Neural Network
    01.04 Autoencoder, Transformers and Generative Adversarial Network

02. AI techniques applied to medical and biomolecular data analysis
    02.01 Types of Biomedical data
    02.02 Recap of data analysis methodologies
    02.03 The PyTorch library
    02.04 CNN Applications for medical imaging
    02.05 RNN Applications for medical signalling processing 

Although there are no mandatory prerequisites, it is advisable to follow this course after completing the courses of Machine Learning and Fundamentals of Artificial Intelligence.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Teaching

Theory lectures and laboratory exercises.

Attendance

Although recommended, course attendance is not mandatory.

Assessment

The exam consists of an oral discussion, where the student presents his/her project with explicit reference to the project goals, the consequent architecture, the implementation choices, providing adequate motivations. A presentation of a written report about the project together with a set of slides for a short presentation of the project is due. 

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

Same as attending.

Attendance

Same as attending.

Course books

Same as attending.

Assessment

The exam consists of an oral discussion, where the student presents his/her project with explicit reference to the project goals, the consequent architecture, the implementation choices, providing adequate motivations. A presentation of a written report about the project together with a set of slides for a short presentation of the project is due. 

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.