Teaching 

Deep Learning, Teaching Assistant

Master Degree in AI and Robotics, Sapienza University of Rome

First Semester, Academic Year 2023/24

Deep Learning has revolutionized both the scientific and industrial worlds. It is one of the most highly sought-after skills in AI with hundreds of thousand job openings each year.

In this course, you will learn the foundations of Deep Learning, understand how to build neural networks using Pytorch. You will learn about Convolutional networks, RNNs, Transformers architectures. You will get to use those architectures in popular applications of Deep Learning such as Transfer Learning, Self-Supervised Learning, Generative Models, Graph Neural Networks, and so on.

Upon completion of the course, students will be able to:

• Understand the fundamental concepts and techniques of supervised and unsupervised learning in Deep Learning.

• Design, implement, and train shallow and deep neural networks.

• Apply advanced techniques such as CNN, Resnets, and Transformers.

• Experiment with self-supervised learning and meta-learning approaches.

• Analyze and implement geometric and equivariant neural network models.

• Evaluate the performance of deep learning models and apply regularization and compression techniques.

• Understand the challenges and solutions related to noise robustness in deep learning models.

• Apply the knowledge gained in hands-on projects using tools like Pytorch and HuggingFace.

Exam

The student's knowledge will be assessed through two main components:

• Project: Throughout the course, a project will be assigned where students must implement and test deep learning algorithms and techniques. This project will account for 70% of the final grade.

• Oral Exam: Students will be assessed on their understanding of the course's key concepts and their ability to discuss and apply them. The oral exam will account for 30% of the final grade.

The final grade will combine these two components, with a specific weight assigned to each based on its relative importance in the course curriculum.

High School Courses on Artificial Intelligence

Fondazione Mondo Digitale, Rome

The aim of these courses is to provide to high school students some notions about basic concepts of AI. Examples are the definition of AGI (Artificial Generative Intelligence), Ethics, LLMs (Large Language Models). Some courses which I had are Coding Girls, Smart & Heart and Ital.IA Lab.