Level: Master Language: Italian Degree: Artificial Intelligence and Cybersecurity Period: 2024-ongoing Semester: Spring (February-July)
This course is part of the Master in Artificial Intelligence and Cybersecurity at Kore University of Enna.
Probability, Decision and Information Theory
Supervised Learning
Linear Models for Regression and Classification
Neural Networks: architectures and training
Unsupervised Learning
The course covers the fundamental concepts of machine learning and deep learning. It is designed to be a mix of theoretical and practical activities.
The final exam include a final project where students are expected to design, implement and evaluate a complete machine learning project for a specific task.
Cybersecurity
Level: Master Language: Italian Degree: Artificial Intelligence and Cybersecurity Period: 2024-ongoing Semester: Spring (February-July)
This course is part of the Master in Artificial Intelligence and Cybersecurity at Kore University of Enna.
Information Security and Risk Management
User Authentication and Access Control
Database Security and Auditing
OS Security and Cryptography
Machine Learning for Cybersecurity and Adversarial Attacks
The course includes advanced topics in cybersecurity and the use of machine learning for security applications.
It aims at providing students with the necessary knowledge to understand and design secure systems and to use machine learning for security applications.
Deep Learning for Speech and Artificial Vision
Level: PhD Language: English Degree: PhD in Computer Engineering Period: 2024-ongoing Month: February
This course is part of the PhD program in Computer Engineering at Kore University of Enna.
The course is focused on Deel Learning architectures for of speech processing and computer vision.
It covers the following topics:
Introduction to deep learning
Convolutional Neural Networks
Transformer architectures and attention mechanisms
Deep Learning libraries for complete project management (PyTorch, Comet ML, HF Transformers, etc.)
Practical application and hands-on projects
The course is designed to be a mix of theoretical and practical activities. At the end of the course, students are expected to be able to design, implement and manage a complete deep learning project in the related fields.
The course material can be found in the following Jupyter Book. The book is a work in progress and it is updated every year.
Deep Natural Language Processing
Level: Master Language: English Degree: Data Science and Engineering Period: 2021-2023 Semester: Winter (September/January) More info:https://github.com/MorenoLaQuatra/DeepNLP
Teaching assistant both for in-class and lab practices.
An unextensive list of course's topics is reported below:
Fundamentals of text processing
Word- and Sentence-level vector spaces
Deep learning architectures (ELMo, BERT and beyond)
Overview and practices with NLP tasks: Machine Translation, Sentiment Analysis, Text Summarization, IR, Chatbots
Level: PhD Course Language: English Period: 2020-2022 Semester: July/September More info:Course website
This is a PhD course covering the fundamental concepts of text mining and natural language processing techniques.
My contribution to the course cover the following topics:
Deep Learning architectures for Natural Language Processing