Introduction to Machine Learning

Period of duration of course
Course info
Number of course hours
Number of hours of lecturers of reference
Number of hours of supplementary teaching

Type of exam

Written exam


View lecturer details


View lecturer details


Students with no background in python are recommended to attend the course "Scientific Programming I: Data Processing and Software Prototyping" by Prof. Bloino


The course will introduce concepts and methods of the two major families of tasks for learning from data. Project work in teams will be assigned along the course.

1) Introduction: the Knowledge Discovery process.

  • All steps in a nutshell.
  • Data understanding and Data exploration: methods and practicals case study on simple data sets iris e titanic

2) Unsupervised learning methods and practicals case study on simple data sets iris e titanic

  • Clustering: intro clustering: K-Means clustering, hints on DBSCAN e Hierarchical clustering
  • Pattern mining and Association Rules: a-priori pattern mining:

3) Supervised Learning: methods and practicals case study on simple data sets iris e titanic

  • Classification: introduction, performance evaluation, a first simple classifier: Decision tree 
  • Overview of advanced methods: Random Forest, Support Vector Machine, Neural Networks
  •  Deep Learning  architecture and exemplar use cases: Recurrent Neural Networks, Generative adversarial networks, Transformers, and Graph Neural Networks 

4) Trustworthy issues on AI systems based on Data Mining and Machine Learning: bias discovery and explainability 

Educational aims

The formidable advances in computing power, data acquisition, data storage, and connectivity have created unprecedented amounts of data. Data mining and Machine Learning, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. The course will introduce the foundations of learning and making predictions from data. The aim of the course is to provide students with basic knowledge of both theoretical foundations and practical aspects of data mining and machine learning with attention to the overall process of extracting knowledge, and its engineering issues

Bibliographical references

  • “Introduction to Data Mining”, 2nd Edition by Tan, Steinbach, Karpatne, Kumar, Berthold et. al. Guide to Intelligent Data Analysis : How to Intelligently make sense of real data Introduction to Machine Learning,
  • Lecture notes. MIT, 2019. tes.pdf
  • Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning. MIT Press, 2016.
  • Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow. A practical handbook to start wrestling with Machine Learning models (2nd ed).