91250 - Deep Learning

Learning outcomes

The course aims at providing advanced skills (both theoretical and practical) on machine learning and, in particular, on deep learning.
At the end of the course the student will be able to:

• in-depth train and optimize deep learning approaches;
• choose and customize the most appropriate techniques to be used in real application scenarios;
• use advanced deep learning techniques.

Course contents

• Introduction to deep learning
• Linear algebra, calculus and automatic differentiation
• Artificial neural networks
• Backpropagation
• Optimization algorithms
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transformers
• AutoEncoders (AE)
  
• Generative models
• Reinforcement Learning (RL)
• Natural Language Processing (NLP) (a practical example)

Readings/Bibliography

Slides of the course.

Suggested reading:

• F. Chollet, "Deep Learning with Python (2nd edition)", Manning Publications Co., USA, 2021.
• A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, "Dive into Deep Learning", 2020.
• M. Elgendy, "Deep Learning for Vision Systems", Manning Publications Co., USA, 2020.
• A. Geron, “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc., USA, 2019.
• M. Nielsen, "Neural Networks and Deep Learning", 2019.
• I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning", MIT Press, 2016.

Teaching methods

Lectures + Practical (guided) sessions in lab.

Note: as concerns the teaching methods of this course unit, all students must attend the online e-learning modules 1 and 2 on health and safety.

Assessment methods

The examination consists of the realization and discussion of a deep learning project and an oral test.

Teaching tools

Software libraries and tools for deep learning:

• Python
• Jupyter
• Tensorflow
• Keras

Office hours

See the website of Matteo Ferrara