All students following the course in the A.Y. 2026/2027 are requested to register in Google Classroom using the e-mail "@studenti.uniroma1.it". The registration code for the course is 73unmyaz

WARNING! The course is open to other students of the Faculty and of the University who are interested in the covered items, as there are NO preparatory or mandatory courses to be taken before.

Lectures will start on Friday the 25th of September and they will be held IN PERSON with the following general time schedule:

• Thursday, hr. 15:00-17:30 (approx.), room 5, via Eudossiana 18, building A-RM031

• Friday, hr. 11:00-12:30 (approx.), room 13, via Eudossiana 18, building A-RM031

N.B.  There are shown actual times of lectures, bearing in mind that 15 minutes per hour are reserved for questions and discussions. Office hours are scheduled by appointment and can be held either in person or remotely.

• Official site of the Master Degree in Industrial/Management Engineering

• Available Master's Theses

• Course objectives and learning outcomes

• Programme A.Y. 2026/2027. The provisional program (to be confirmed at the end of the course) is referring to ALL and ONLY what was presented, explained and discussed during lectures:

  • • • Fundamentals of Machine Learning Methods

      • Introduction to Quantum Computing

      • Quantum Optimization

      • Variational Quantum Circuits

      • Quantum Machine Learning and Quantum Deep Learning

      • Hyperdimensional Computing

      • Applications to Real-World Problems and Complex Systems

      • Hands-on practices using Python and Matlab:

        • • quantum programming and simulation;

          • quantum optimization;

          • variational circuits and quanutm deep learning;

          • quantum generative models;

          • deep learning;

          • energy time series prediction; 

          • graph neural networks.

      • Applications and case studies:

        • • prediction of renewable energy sources, intelligent energy systems, smart grids;

          • applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.);

          • behavioral analysis and biometrics;

          • analysis of materials and industrial processes;

          • machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks;

          • federated and distributed learning systems;

          • quantum neural networks, quantum optimization, and quantum generative models. 

  • Exams Timetable A.Y. 2026/2027. Exams may be taken by appointment when it is deemed most appropriate starting from January 2026; the exam registration will take place in the official time windows provided by the Faculty calendar, as shown below:

    • • 1st round: January 2027

      • 2nd round: February 2027

      • Extra round: March/April 2027
        NOTE. Reserved to the categories of students indicated in the Examinations, qualifying examinations, internships, other educational activities.
        NO EXCEPTIONS ARE ALLOWED.

      • 3rd round: June 2027

      • 4th round: July 2027

      • 5th round: September 2027

      • Extra round: October/November 2027
        NOTE. Reserved to the categories of students indicated in the Examinations, qualifying examinations, internships, other educational activities, as well as to failing students and to students enrolled for A.Y. 2026/2027 in the 2nd year of the Master Degree.
        NO EXCEPTIONS ARE ALLOWED.

  • Teaching Material:

    • • E. F. Combarro & S. González-Castillo, A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms, Packt Publishing, 2023

      • M. Schuld & F. Petruccione, Machine Learning with Quantum Computers (2nd edition), Springer, 2021

      • Notes, slides and handouts provided by the Teachers (see the program references):
              TBD

      • Additional material on hands-on and case studies:
              TBD  

      • Further reading (optional):

        • • C.C. Aggarwal,  Neural Networks and Deep Learning, Springer Cham, 2023

          • S. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson, 2009

          • M. Schuld & F. Petruccione, Supervised Learning with Quantum Computers, Springer Nature, 2018

          • O. Simeone, An Introduction to Quantum Machine Learning for Engineers, arXiv preprint [2205.09510], 2022

NOTICE. For each type of communication or inquiries related to the course, students are kindly requested to send me an e-mail writing in the SUBJECT "Neural Networks IE" and in the text body the following data: name, surname and university ID number. I will try to answer as soon as possible.