The course aims to introduce the student to recent advances and the application of deep learning. To this end, case studies from the fields of economics, finance and law will be introduced.

01. Libraries for machine learning 
  01.01 Python and Scikit Learn
  01.02 Tensorflow and pyTorch
02. Deep Learning 
  02.01 Recurrent Neural Networks
  02.02 Convolutional Neural Networks
  02.03 Applications in the fintech domain
03. Neural Networks on Graphs
  03.01 Introductions to complex networks
  03.02 Graph Neural Networks
  03.03 Application in the Law and Financial domains, e.g., Anti Money Laundering in cryptocurrencies

machine learning

Knowledge and Understanding: Upon completion of this course, the student understands the fundamental ideas, recent advances, and application potential of deep neural systems. The student understands basic neural topologies, methods for visualizing and understanding the behavior of neural networks, recurrent networks, and networks on graphs. The student is able to apply these technologies to solving classification problems in realistic domains.

Ability to apply knowledge and understanding: the student will be able to apply acquired knowledge with the goal of designing machine learning systems for classification and prediction.

Autonomy of judgment: the student will be able to evaluate the efficiency of a learning system.

Communication skills: the student will be able to appropriately explain the descriptive features of neural network system, the functionality of the related system..Learning skills: the student will learn the ability to design and implement machine learning systems.

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

Lectures and lab 

Attendance

Although strongly recommended, attendance is not mandatory.

Assessment

Project

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

Lectures and lab 

Attendance

Not required

Assessment

Project

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.