Modulo BASIC

Academic Year 2023/2024 - Docente: SEBASTIANO BATTIATO

Risultati di apprendimento attesi

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:

  • Lectures, to provide theoretical and methodological knowledge of the subject;

  • Hands-on exercises, to provide “problem solving” skills and to apply design methodology;

  • Laboratories, to learn and test the usage of related tools.

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.

Required Prerequisites

Basic Calculus anf Math

Algebra and Matrix Notation

Machine learning basic principle

Python programming language

Attendance of Lessons

Strongly reccomended

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

Textbook Information


Dive into Deep Learning (2020)


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

Course Planning

 SubjectsText References
1Logistic RegressionGlassner (vol .1, vol 2)
2BacKpropagationGlassner (vol .1, vol 2)
3Supervised vs Unsupervised LearningGlassner (vol .1, vol 2)
4Neural Network principlesBishop
5Convolutional Neural NetworksDive into Deep Learning

Learning Assessment

Learning Assessment Procedures

Writtten and Oral Examination

Learning assessment may also be carried out on line, should the conditions require it.

Examples of frequently asked questions and / or exercises

Example of Algorithms based on training data

Cross Validation

NN Architecutre