NETWORK INTELLIGENCE

Academic Year 2025/2026 - Teacher: Marco SIINO

Expected Learning Outcomes

Knowledge of basic and advanced theoretical aspects of Artificial Intelligence applied to the network domain. Complete knowledge of Machine Learning theory. Knowledge of the libraries and languages most used for Deep Learning.

Knowledge and understanding: Taking advantage of the knowledge acquired, the student will be able to apply the theoretical foundations inherent to the development of Deep Learning models for telecommunications and networks.

Applied knowledge and understanding: By taking advantage of the skills acquired in the course, students will be able to identify the main requirements for the specific application context and to identify and design the most appropriate solutions.

At the end of the course, students will be able to identify the best solutions for the design and development of Artificial Intelligence models to be applied to the networking domain.

Required Prerequisites

To successfully follow the course, students are expected to have a solid background in the following areas:

Mathematics: knowledge of linear algebra, differential and integral calculus, probability, and basic statistics, essential for understanding machine learning models.
Computer Science: programming skills in Python and familiarity with libraries for numerical computation and data analysis (e.g., NumPy, Pandas, Matplotlib).
Fundamentals of Machine Learning: understanding of introductory concepts related to supervised and unsupervised models, classification, regression, and clustering methods.
Telecommunication Networks: basic knowledge of principles and architectures of communication networks, necessary to contextualize the applications of Artificial Intelligence in networking.

Prior exposure to Deep Learning libraries (e.g., TensorFlow, PyTorch) and data analysis environments (Jupyter Notebook or equivalent) is recommended but not strictly required.

Attendance of Lessons

Not mandatory.

Detailed Course Content

Topics

The course is structured into four main areas of interest:

Foundations of Machine Learning

Introduction to Machine Learning: definitions, techniques, and main algorithms.
Supervised and unsupervised learning: classification, regression, clustering.
Classical algorithms: decision trees, support vector machines, K-means, basic neural networks.

Deep Learning and Neural Networks

Fundamentals of Deep Learning: multilayer neural networks, activation functions, and backpropagation.
Advanced architectures: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM).
Training and optimization techniques: dropout, batch normalization, optimizers such as Adam and SGD.
Transformer architectures: attention mechanisms, sequential and generative models.

Prompt Engineering and Generative Models

Core concepts of prompt engineering: principles, strategies, and prompt design techniques.
Applications of Transformer-based models (e.g., BERT, GPT) in the context of telecommunication networks.
Leveraging Large Language Models (LLMs) for analysis, automation, and decision support in networking scenarios.

Reinforcement Learning and Applications

Basics of Reinforcement Learning: agents, environments, rewards, and action policies.
Key algorithms: Q-learning, Deep Q-Networks (DQN), Actor-Critics (A2C, PPO).
RL applications in networking: resource optimization, intelligent routing, traffic management.

Applications in Networking
Students will explore the application of the above techniques in real-world communication network scenarios. Examples include:

Network performance optimization: using ML and DL algorithms to predict and improve quality of service.
Traffic management: applying DL, RL, and Transformer models for dynamic resource allocation.
Network security: intrusion and cyberattack detection through predictive and generative models.
Network automation: employing Reinforcement Learning and Prompt Engineering to automate the control of complex networks.

Textbook Information

"Pattern Recognition and Machine Learning", Christopher M. Bishop
"Deep Learning", Ian Goodfellow, Yoshua Bengio e Aaron Courville
"Understanding Machine Learning: From Theory to Algorithms", Shai Shalev-Shwartz e Shai Ben-David
"Reinforcement Learning: An Introduction", Richard S. Sutton e Andrew G. Barto
Slides

Learning Assessment

Learning Assessment Procedures

The exam includes two alternative options, to be chosen by the student:

OPTION A — Project + Oral

Submission of the project by June.
Oral discussion of the project by the September exam session.

OPTION B — Written + Oral

Written exam on all the topics covered in the course.
Oral exam for further discussion following the written test (3 questions).

Examples of frequently asked questions and / or exercises

Explain the difference between supervised and unsupervised learning, providing examples of applications in the context of communication networks.

How does the Support Vector Machine (SVM) algorithm work, and how can it be applied to classify network traffic?

Describe the backpropagation process in a deep neural network and its importance in model training.

What are the main differences between Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)? Provide an example of an application of each in the network domain.

How is the Q-learning algorithm used in Reinforcement Learning to manage network resource allocation?

Explain how dropout and batch normalization improve the training of a deep neural network.

How can Deep Learning be used to enhance network security and detect intrusions?

What role do optimizers like Adam or SGD play in the training of Deep Learning models, and how do they affect convergence?

Describe a practical application of Reinforcement Learning in network automation.

What challenges arise when applying Machine Learning to real-time network traffic management?

Degree Course in

Communications Engineering