NEURAL COMPUTING

INF/01 - 6 CFU - 1° Semester

Teaching Staff

SEBASTIANO BATTIATO
Email: battiato@dmi.unict.it
Office: Dipartimento di Matematica e Informatica - Viale Andrea Doria 6
Phone: +390957383206
Office Hours: Consultare il sito web del docente (See personal home page)


Learning Objectives

The course covers the theory and practice of artificial neural networks, highlighting their relevance both for artificial intelligence applications and for modeling human cognition and brain function. Theoretical discussion of various types of neural networks and learning algorithms is complemented by hands-on practices in the computer lab. Models for classification and regression, as well as neural network architectures (e.g., Deep Learning) will be discussed. The course will present the techniques to design and optimize learning algorithms, and those useful to assess the performance of Machine Learning systems.


Course Structure

The main teaching methods are as follows:

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.



Detailed Course Content

Linear Models for Regression: Linear Models for Classification: Gradient Descent, Multi-Class Classification, Classifiers Evaluation

Neural models and Network Architectures

Basic neural network models: multilayer perceptron, distance or similarity based neural networks, associative memory and self-organizing feature map, radial basis function based multilayer perceptron, neural network decision trees, etc.

Basic learning algorithms: the delta learning rule, the back propagation algorithm, self-organization learning, etc.

Supervised Learning with Neural Networks

Deep Learning: Convolutional Neural Network

Python programming and Python Libraries for Machine Learning



Textbook Information

DEEP LEARNING FROM BASICS TO PRACTICE (2020)

https://www.glassner.com/portfolio/deep-learning-from-basics-to-practice/

Dive into Deep Learning (2020)

https://d2l.ai/d2l-en.pdf

OTHER

E. Alpaydin, “Introduction to Machine Learning”, MIT Press, 2014

I. Goodfellow, Y. Bengio and A. Courville, "Deep Learning", MIT Press, 2016

M. P. Deisenroth, A A. Faisal, and C. Soon On, Mathematics for Machine Learning, MIT Press, 2019




Open in PDF format Versione in italiano