81610 - Machine Learning

Learning outcomes

Providing the student with the concepts necessary: - to understand and apply machine learning approaches; - implement classification, regression and clustering algorithms to solve problems in different applicative fields; use neural networks and other deep learning techniques. 

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)
• Reinforcement Learning (RL)

Readings/Bibliography

Teacher's slides at:

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

Teaching methods

Lectures + Practical (guided) sessions in lab.

Lab assignments and solutions at:

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

Assessment methods

Written exam

Teaching tools

Software libraries and tools for machine learning:

- Scikit-learn (Python)

- Tensorflow, PyTorch, Caffè 

Links to further information

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

Office hours

See the website of Davide Maltoni