n.

Teaching title

N.  hours

Teaching Professor

Recommended PhD year for attendance

Course description

Schedule

Notes

1.

Foundations of Modelling and Design of Complex Systems

8 + 4

A.G. Bruzzone

1st year

Foundation on Complex Systems. Transfer of knowledge about Simulation Paradigms and Modeling Methodologies effective for addressing Complex Systems. Transfer of capabilities to analyze real problems and case studies corresponding to Complex Systems. Acquisition of skills in Conceptual Modeling applied to Complex Problems.

Acquisition of Skills in design of Simulation Architectures and Model Development applied to Complex Systems.

September 25, 2023, 

14:00-18:00,  October 2, 

2023, 

14:00-18:00 

Compulsory teaching

2.

Foundations of Mathematical Modeling &

Continuous / Discrete Simulation

8 + 4

A. G. Bruzzone

1st or 2nd year

The course aims to provide a presentation of the most common partial differential equations (PDE) and their solution techniques through an analysis of various applications. The emphasis is devoted to second order PDE and the understanding of the specific techniques for elliptic, parabolic and hyperbolic cases.

March 18, 

2024, 

14:00-18:00,  March 25, 

2024, 

14:00-18:00 

Compulsory teaching

3.

Computational Intelligence - Machine Learning for Decision Making 

12

F. Bellotti

1st or 2nd year

Contents: Neural networks; fuzzy logic systems; evolutionary computing; swarm intelligence; neuro-fuzzy and fuzzy neural systems; hybrid intelligent systems, machine learning; classification, regression learning, clustering.

July 2024; 

day to be defined

Compulsory teaching

4.

Foundations of Linear programming and network optimization with spreadsheet optimization

8

A. Sciomachen

1st year

Lessons will cover Linear Programming Problems and network optimization problems, the related basic theory, the structure of the related models. Multi period and multi-level problems will be faced. Case studies will be formulated and solved with Excel. Participants will be asked to develop and analyse a case study.

November 29, 2023, 

10:00-14:00,

November 30, 2023, 

14:00-18:00

Compulsory teaching

5.

Foundations of Programming for Problem Solving in Python

8

C. Cerrone

1st year

PhD Students have to follow the lessons on the fundamental concepts of the Course as well as to discuss a Project Work to be finalized and discussed

December 4, 2023,

 09:30-13:30,

December 5, 2023, 

9:30-13:30

Compulsory teaching

6.

Strategic Engineering: The Closed Loop M&S, Data Analytics & AI at Work

8

A. G. Bruzzone

2nd year

The Course presents the main Principia of Strategic Engineering as well as examples and case studies related to this innovative discipline based on the closed loop among Data Analytics, AI, M&S and Big Data from multiple real and virtual Sources

Optional teaching

7.

Discrete Event Simulation Models for Strategic Decisions

8

A. Sciomachen

2nd year

The Course presents how Discrete Event Simulation could be effectively used to

support Strategic Decision Making

Optional teaching

8.

Strategic Planning for Logistics and Transportations

8

E. M. Cepolina

2nd year

Models and Methodologies to address Strategic Planning

in Logistics and Transportations

Optional teaching

9.

International Law for Conflicts and Cyber Security

8

S. Dominelli

2nd year

The Course addresses the International Law as well as up to date advances in this sector with special attention to Crisis, Armed Conflicts and Cyber Security/Defense.

Optional teaching

10.

Geomatics & Strategic Decision Makers

8

I. Ferrando

2nd year

The Course provides foundations of Geomatics with special attention to Strategic Decisions

22-26 

January 2024

Optional teaching

11.

Paper writing

12

M. Marchese

1st year

The course aims to provide some basic elements to: choose a research topic; manage and use sources; do a novel, serious, and useful research; describe and explain a research.

January 2024,

days to  be defined

Optional teaching

12.

Math-heuristics in Python

8

C. Cerrone

2nd year

The Course presents advanced use of Python to implement models and heuristic algorithms to address complex problems

Optional teaching

13

Explanable AI

T. Cerquitelli

2nd year

Understanding the inner workings of a model and the reason for its decisions is important for trusting the outcome of a machine-learning process. However, many machine learning models do not reveal their internal logic leading to predictions, so they are called "black box models." The explainability of a model in its many facets contributes to the robustness and reliability of any machine learning application. It supports most phases, from design to deployment of ML applications, from model validation and testing to model debugging and verification. In addition, explaining the results of ML algorithms can help end users understand the reason for a decision and trust the model's outcome. This course will introduce modern explanation methods for ML algorithms. Participants will be asked to perform a hands-on activity on the course topic and write a short report.

Early July 2024

Optional teaching