88202 - Machine Learning (2nd cycle)

Course contents

• Artificial Intelligence and Machine Learning
• Supervided and Unsupervised Learning
• Classification and Regression
• Classifiers: Bayes, k-Nearest Neighbor, Support Vector Machines, Multiclassifiers
• Clustering (K-means, EM) and Dimensionality Reduction (PCA, DA)
• Neural Networks (NN)
• Introduction to Deep Learning
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transformers and Large Language Models (LLM)
• Reinforcement Learning (RL)

Readings/Bibliography

Teacher's slides available on Virtuale

Teaching methods

Lectures + Practical (guided) sessions in lab.

Lab assignments and solutions at:

http://bias.csr.unibo.it/maltoni/ml

Note: As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online

Assessment methods

Written exam with exercises and questions with free text answers.

The exam duration is 90 minutes.

Examples (previous exams with corrections) are available in the course web page.

It is not permitted to use books, teacher slides and notes.

A simple scientific calculator can be used (no smartphones).

The exam score is computed as the sum of single exercise/question scores. The scores of single exercises/questions can be slightly different based on their relative difficulty. If the total score exceeds 30, the final grade is 30 lode.

Teaching tools

Software libraries and tools for machine learning:

- Scikit-learn (Python)

- Tensorflow, PyTorch

Office hours

See the website of Davide Maltoni