1. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.

. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.

Database Basics: Fundamental Properties - Textbook, Chapter 1 (entire)
Relational Model - Textbook, Chapter 2 (entire)
Relational Algebra - Textbook, Chapter 3 (excluding sections 3.2 and 3.3)
SQL - Textbook, Chapters 4 (excluding sections 4.2.1, 4.2.4, 4.2.5, 4.2.9) and 5 (excluding sections 5.1.2, 5.1.6, 5.2, 5.3, 5.4)
E-R Model - Textbook, Chapter 6 (excluding section 6.4)
Conceptual Design - Textbook, Chapter 7 (excluding section 7.7)
Logical Design - Textbook, Chapter 8 (excluding section 8.6)
Normalization - Textbook, Chapter 9 (excluding sections 9.4.2, 9.4.3, 9.5, and 9.6)

P. Atzeni, S. Ceri, S. Paraboschi, R. Torlone: Database Systems - Concepts, Languages and Architectures, The McGraw-Hill Companies, 1999.

Students learn methodological elements of Social Network Analysis, combined with other analytical tools such as Text Mining, applied to economic and business contexts. The study is not only theoretical but also focused on the practical application of these techniques through the use of specific software tools.

In the second part, the course focuses on acquiring managerial and strategic knowledge, including the study of leadership principles, team management, strategic communication techniques, and problem-solving for change management, as well as design thinking and innovation management.

Below is a more detailed overview of the course content.

Part I: Business Analytics
- Introduction to Social Network Analysis and complex systems
- Network relationships and data collection
- Centrality measures
- Random networks, small-world and scale-free
- Clustering and structural holes
- Network communities
- Key roles in knowledge exchange networks
- Robustness and resilience
- Introduction to diffusion models
- Autocorrelation and QAP
- Introduction to big data analytics
- Honest signals
- Semantic Brand Score

Part II: Business Management
- Problem solving
- Change management
- Leadership
- Teambuilding
- Design Thinking for business strategy and innovation

Software:
- SBS BI, Pajek and Gephi.

Both modules include case studies and practical exercises.

A more detailed program is provided by the teacher on the Moodle platform.

Fronzetti Colladon, A., & Grippa, F. (2025). Leading Meaningful Change. Edward Elgar.

Fronzetti Colladon, A., & Vestrelli, R. (2025). Social Network Analysis and Text Mining for Big Data: The Power of Words and Networks. Routledge.

Elective reading:
- Brown, T. Change by Design, Revised and Updated. ‎Harper Business
- Verganti, R. Overcrowded: Designing Meaningful Products in a World Awash with Ideas. The MIT Press.

All the materials made available by the teacher on the Moodle platform are part of the course and their study is necessary to pass the final exam. They are provided in addition to the textbooks listed here.

Dynamical models of stationary processes and prediction
- Physical laws in engineering and science
- Stochastic processes
- Models for filtering, prediction and control: Input-output models for time series and dynamical systems (AR, ARMA, ARX, ARMAX)

Identification
- Black-box identification (Least Squares and Maximum likelihood methods)
- Model complexity selection
- Cross-validation, FPE (Final Prediction Error), AIC (Akaike Information Criterion) or MDL (Minimum Description Length) techniques
- Recursive identification methods (RLS,ELS,RML). Adaptation via forgetting factor techniques

Bayesian filtering
- The state estimation problem. Filtering, prediction and smoothing.
- Kalman filter, steady-state filter Extended Kalman filter
- Unscented transformation, Unscented Kalman filter
- Grid-based filtering
- Particle filtering

Distributed filtering
- Information filter
- Extended Information filter

Sergio Bittanti, "Model Identification and Data Analysis", John Wiley and Sons Ltd

The industrial enterprise
Organization and scope of industrial enterprise. Classification of production systems. Analysis of production processes (process mapping and performance estimation). Little’s law. Impact of flow and process variability on main performance measures. Analysis of lot size effects on capacity, lead time and manufacturing cost. Lot sizing criteria in repetitive manufacturing.

Demand forecast
Analysis of demand variability components (random fluctuations, trends, seasonality). Classification of quantitative and qualitative forecasting methods. Linear regression causal models, time series methods (moving averages, exponential smoothing) and seasonal forecasting methods. Estimation of forecast error. Demand estimation for new products: market size and market penetration dynamics (Bass model).

Fundamentals of production planning and control
Analysis of P-Time and D-Time. Push and Pull production systems. Make to Stock, Assemble to Order, Make to Order ed Engineering to Order systems. The hierarchical production planning framework.

Aggregate planning
Alternatives to match production and demand. Trial and error aggregate planning methods (chase, level and mixed plans). LP models for the aggregate planning problem.

Master production scheduling
Criteria to disaggregate an aggregate plan and methods to develop a Master Production Schedule (MPS) based on items forecast and firm orders. Etsimation of Available to Promise capacity. Difference of MPS in MTS and ATO settings.

Requirements planning
MRPI and II methods. Capacity Requirements Planning. Lot sizing criteria for materials requirements planning. Limitations of MRP systems.

Operational planning and manufacturing execution
Final Assembly Schedule and operational plans. Criteria for job release and queues control. Heuristic rules for job scheduling and priority assignment. Production advancement and control systems.

Pull production systems
Kanban method and production leveling techniques. Methods for sequencing mixed model assembly lines. CONWIP. Comparison of push and pull systems.

Inventory management
Classification and scope of inventories. Relevant costs in inventory management. Management of dependent demand materials: economic order quantitym reorder cycle and reorder level policies. Service level and computation of safety stock. Benefits of safety stock pooling. Management of dependent demand items: lot by lot and dynamic lot sizing techniques. Newsboy model and single period order sizing. ABC classification and warehouse performance measures.

Lecture notes provided by instructor and uploaded on Moodle web site.

Interdipendency and complexity within infrastructural systems and in emergency management. CISIApro 2.0. NIS directive. Control rooms. GDPR. Introduction to risk analysis in interdependent systems. MHR modelling. Complex networks.

Vulnerability of industrial control systems. Cyber attacks to ICS. Smart Behavioral Filter. Hands-on ICS.

Decision Support Systems. Building Automation Systems. Smart Cities. Introduction to IoT. IoT Database. Iot Cloud.

Notes of the professor

Mathematical modelling software and/or other software for the development of an individual project in decision-making

For ampl software:
- https://ampl.com/resources/the-ampl-book/
- lecturer's slides
For other softwares:
- notes available on the web

1. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.

. Introduction:
- Intelligent Agents.
- AI as "Representation and Search".
2. Problem Solving:
- Uninformed search (breadth-first search, uniform-cost search, depth-first search, Iterative deepening search).
- Heuristic search (Best First search, A *, Heuristic Functions).
- Approximate algorithms (Hill Climbing, Simulated Annealing, etc.)
- Adversarial Search and Games (MiniMax, Alfa-Beta Pruning).
- Introduction to Evolutionary Computation.
3. Introduction to the Python language:
- Development environments; Jupiter Notebook.
- Python foundations. Data structures in Pyhton.
- Python libraries: NumPy, Pandas, matplotlib, ScikitLearn.
4. Machine Learning:
- Regression (simple linear, multiple).
- Classification (Logistic Regression, Decision Trees, Naïve Bayes).
- Clustering.
- Artificial Neural Networks.
- Reinforcement Learning.
- Introduction to Deep Learning.
- Case studies.

- Introduction to Operating Systems
- Processor virtualization
- Memory virtualization
- Concurrent programming
- Storage virtualization

Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau, Operating Systems: Three Easy Pieces, https://pages.cs.wisc.edu/~remzi/OSTEP/

Database Basics: Fundamental Properties - Textbook, Chapter 1 (entire)
Relational Model - Textbook, Chapter 2 (entire)
Relational Algebra - Textbook, Chapter 3 (excluding sections 3.2 and 3.3)
SQL - Textbook, Chapters 4 (excluding sections 4.2.1, 4.2.4, 4.2.5, 4.2.9) and 5 (excluding sections 5.1.2, 5.1.6, 5.2, 5.3, 5.4)
E-R Model - Textbook, Chapter 6 (excluding section 6.4)
Conceptual Design - Textbook, Chapter 7 (excluding section 7.7)
Logical Design - Textbook, Chapter 8 (excluding section 8.6)
Normalization - Textbook, Chapter 9 (excluding sections 9.4.2, 9.4.3, 9.5, and 9.6)

P. Atzeni, S. Ceri, S. Paraboschi, R. Torlone: Database Systems - Concepts, Languages and Architectures, The McGraw-Hill Companies, 1999.

Short introduction to electrical drives: functional block schemes, review on electrical machine types, AC and DC, quadrants of operations, regenerative brake, thermal behaviors, analysis in the Laplace domain.
Power electronics converters for electrical drives: topologies, characteristics and modulation techniques.
Short view on DC electric drives: torque and flux control, speed control and position control.

Electrical drives based on synchronous machine: electrical machine operation and drive block scheme, dynamic model in the synchronous reference frame, operation under sinusoidal supply, control strategies for the related electrical drives.

Electrical drives with induction machine: electrical machine operation and drive block scheme, dynamic model in the synchronous reference frame, scalar and vector control strategies for induction machine based electrical drives.

In addition to the lecture notes provided by the teacher:

Ion Boldea, Syed A. Nasar
Electric Drives, Third Edition 2016 by CRC Press
ISBN 9781498748209

Bimal K. Bose
Modern Power Electronics and AC Drives
Prentice Hall PTR, 2002

Ned Mohan, Tore M. Undeland, William P. Robbins
Power Electronics: Converters, Applications, and Design
ISBN: 0471226939

Ned Mohan
Advanced Electric Drives: Analysis, Control, and Modeling Using MATLAB / Simulink
ISBN: 978-1-118-48548-4

Ned Mohan
Electric Drives: An Integrative Approach
ISBN: 0971529256

Course programme
1. Introduction to Multi-Criteria Decision Making for Public Service Management Problems
2. Optimization in Project Planning and Scheduling
3. Disjunctive Programming: Scheduling and Routing Models, (Meta)Exact /Heuristic Algorithms
4. Reservations Systems and Interval Scheduling
5. Timetabling with Operator or Tooling Constraints
6. Scheduling and Timetabling in Sport Tournaments
7. Scheduling Network Television Programs
8. Transportation Problems: Tanker/Aircraft/Train Coordination, Scheduling and Routing
9. Decision Support Systems for Real-Time Dispatching of Operations
10. Workforce Scheduling: Days-Off Scheduling, Shift Scheduling, Cyclic Staffing
11. Airline Crew Scheduling
12. Discrete Location Problems
13. Water and Air Quality Management
14. Health Care Delivery

Michael L. Pinedo (Author) “Planning and Scheduling in Manufacturing and Services”, Springer Series in Operations Research, Edition 2005
S.M. Pollock, M.H. Rothkopf, A. Barnett (Editors), “Operations Research and the Public Sector”, Handbooks in Operations Research and Management Science, Volume 6, Edition 1994

The course is designed to introduce key concepts of strategic management and marketing, combined with elements of economics and business. Students are also guided in applying these concepts to real business cases.

In detail, the main topics covered include:
- Strategic marketing framework (5C) and G-STIC action plan
- Porter’s Five Forces and SWOT analysis
- Marketing strategy and tactics
- Consumer behavior, customer value creation, and CRM
- Market targeting and segmentation
- Creating value for the company and its stakeholders
- Gaining and defending market position
- Product and service management
- Brand management
- Communication and incentive management
- Persuasion and cognitive biases in sales and managerial decisions
- Pricing management
- Personal selling and retail sales strategies
- Creativity, innovation, and development

The course also includes case studies and practical exercises.

A more detailed syllabus is provided by the instructor on the Moodle platform.

Chernev, A. (2019). Strategic Marketing Management: Theory and Practice. Cerebellum Press.

La Bella, A. & Capece, G. Manuale di Direzione d'Impresa, Franco Angeli. (ONLY THE CHAPTER NAMED "Creatività, Innovazione e Sviluppo")

Elective reading:
- Roberts, K. Lovemarks, powerHouse Books
- Cialdini, R. Influence: The Psychology of Persuasion, Revised Edition. Harper Business

All the materials made available by the teacher on the Moodle platform are part of the course and their study is necessary to pass the final exam. They are provided in addition to the textbooks listed here.

Interdipendency and complexity within infrastructural systems and in emergency management. CISIApro 2.0. NIS directive. Control rooms. GDPR. Introduction to risk analysis in interdependent systems. MHR modelling. Complex networks.

Vulnerability of industrial control systems. Cyber attacks to ICS. Smart Behavioral Filter. Hands-on ICS.

Decision Support Systems. Building Automation Systems. Smart Cities. Introduction to IoT. IoT Database. Iot Cloud.

Notes of the professor

The industrial enterprise
Organization and scope of industrial enterprise. Classification of production systems. Analysis of production processes (process mapping and performance estimation). Little’s law. Impact of flow and process variability on main performance measures. Analysis of lot size effects on capacity, lead time and manufacturing cost. Lot sizing criteria in repetitive manufacturing.

Demand forecast
Analysis of demand variability components (random fluctuations, trends, seasonality). Classification of quantitative and qualitative forecasting methods. Linear regression causal models, time series methods (moving averages, exponential smoothing) and seasonal forecasting methods. Estimation of forecast error. Demand estimation for new products: market size and market penetration dynamics (Bass model).

Fundamentals of production planning and control
Analysis of P-Time and D-Time. Push and Pull production systems. Make to Stock, Assemble to Order, Make to Order ed Engineering to Order systems. The hierarchical production planning framework.

Aggregate planning
Alternatives to match production and demand. Trial and error aggregate planning methods (chase, level and mixed plans). LP models for the aggregate planning problem.

Master production scheduling
Criteria to disaggregate an aggregate plan and methods to develop a Master Production Schedule (MPS) based on items forecast and firm orders. Etsimation of Available to Promise capacity. Difference of MPS in MTS and ATO settings.

Requirements planning
MRPI and II methods. Capacity Requirements Planning. Lot sizing criteria for materials requirements planning. Limitations of MRP systems.

Operational planning and manufacturing execution
Final Assembly Schedule and operational plans. Criteria for job release and queues control. Heuristic rules for job scheduling and priority assignment. Production advancement and control systems.

Pull production systems
Kanban method and production leveling techniques. Methods for sequencing mixed model assembly lines. CONWIP. Comparison of push and pull systems.

Inventory management
Classification and scope of inventories. Relevant costs in inventory management. Management of dependent demand materials: economic order quantitym reorder cycle and reorder level policies. Service level and computation of safety stock. Benefits of safety stock pooling. Management of dependent demand items: lot by lot and dynamic lot sizing techniques. Newsboy model and single period order sizing. ABC classification and warehouse performance measures.

Lecture notes provided by instructor and uploaded on Moodle web site.

Introduction to multi-agent systems

Review of matrix theory with emphasis on Perron-Frobenius theory

Graph theory and algebraic graph theory

Stability analysis for connected systems

Examples of coordination of multi-robot systems

Introduction to Model Predictive Control and mathematical fundamentals

Formulation of the MPC Problem

Stability of MPC

MPC for Multi-agent Systems

- F. Bullo, Lectures on Network Systems, CreateSpace Independent Publishing Platform, ISBN ‎978-1986425643, 2022

- Camacho, E. F., & Bordons, C., Model Predictive control, Springer, ISBN: 978-1-85233-694-3, 2013.

- Lecture notes

- Introduction to multi-agent systems
- Review of matrix theory with emphasis on Perron-Frobenius theory
- Graph theory and algebraic graph theory
- Stability analysis for connected systems
- Examples of coordination of multi-robot systems
- Introduction to Model Predictive Control and mathematical fundamentals
- Formulation of the MPC Problem
- Stability of MPC
- MPC for Multi-agent Systems

- F. Bullo, Lectures on Network Systems, CreateSpace Independent Publishing Platform, ISBN ‎978-1986425643, 2022
- Camacho, E. F., & Bordons, C., Model Predictive control, Springer, ISBN: 978-1-85233-694-3, 2013.
- Lecture notes

1. An Introduction to Robot Operating System (ROS)
2. Understanding ROS Nodes, Messages, Topics, Services and Parameters
3. Understanding Gazebo Simulation Environment
4. Understanding the Differential Drive Robot Kinematics
5. Understanding the ROS GUI Tools (Rviz and Rqt)
6. Understanding the Navigation Stack

Course Textbook
- Lentin Joseph. 2018. Robot Operating System for Absolute Beginners. Apress, USA.

Recommended Textbook
- Lentin Joseph and Jonathan Cacace. 2018. Mastering ROS for Robotics Programming - Second Edition: Design, build, and simulate complex robots using the Robot Operating System (2nd. ed.). Packt Publishing.

Mathematical modelling software and/or other software for the development of an individual project in decision-making

For ampl software:
- https://ampl.com/resources/the-ampl-book/
- lecturer's slides
For other softwares:
- notes available on the web