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Machine Learning and its Application to Chemistry and Materials Science


Machine Learning and its Application to Chemistry and Materials Science


Academic year 2024/2025

Course ID
Alessandro Erba (Lecturer)
Eugenio Alladio (Lecturer)
Degree course
Materials Science [0208M21]
1st year, 2nd year
Teaching period
First semester
Course disciplinary sector (SSD)
CHIM/01 - analytical chemistry
CHIM/02 - physical chemistry
Class Lecture
Type of examination
Written and oral
Type of learning unit

Sommario del corso


Course objectives

The course aims at providing the student with basic concepts of machine learning and at discussing its possible applications in the fields of chemistry and materials science. In particular, the student will learn about the main methods and algorithms of supervised and unsupervised learning (both for regression and classification purposes). Hands-on sessions will provide the student with practical guidelines on how to actually perform machine learning experiments for real case studies (using Python-based tools).


Results of learning outcomes

  • Basic concepts of statistics;
  • Familiarity with the “jargon” of machine learning;
  • Knowledge of the main machine learning approaches for supervised learning (regression, discriminant analysis, decision trees, neural networks, support vector machines);
  • Knowledge of the main machine learning approaches for unsupervised learning (principal components analysis; clustering; self-organizing maps);
  • Designing a machine learning experiment for specific applications in chemistry and materials science (data, features  and algorithm selection; validation);
  • Knowledge of available tools related to machine learning from Python.


  • Introduction (what is machine learning?; Big data in the context of machine learning; general concepts: classification, regression, supervised, unsupervised and reinforcement learning)
  • Design and analysis of machine learning experiments (features selection; data pre-processing; model selection; validation and cross-validation; bias and variance; overfitting and underfitting;)
  • Supervised learning (linear and multivariate regression; linear and quadratic discriminant analysis; nearest-neighbour approaches; decision trees; elements of neural networks and deep learning; support vector machines)
  • Unsupervised learning (principal components analysis; clustering; self-organizing maps)
  • Elements of univariate and multivariate statistics (probability distribution functions, maximum likelihood estimation, parametric vs. non-parametric models; hypothesis tests)
  • Application of selected algorithms to case studies in chemistry and materials science (with Python)

Course delivery

The Course consists in frontal lessons, systematically complemented by hands-on sessions, where all concepts are applied by use of available Python-based tools after being formally presented and discussed. [total of 32 hours]

All classes will take place face-to-face (Lecture Hall TBD

All didactic material will be published on the Moodle platform.

The Course has a strong hands-on character so all students will need to bring a laptop to class.

All students are kindly asked to register for the Course using the relevant item in the menu bar at the bottom of this page, in order to receive relevant communications about the Course and what needs to be installed on your computer. 


Learning assessment methods

Written and oral exams (both mandatory).

The exam consists in:

1) Written - The written test consists in the preparation of a technical report (to be prepared remotely with the Python tools for Machine Learning discussed throughout the Course) on a specific exercise to be individually assigned to each student by the teachers. The exercise will be sent at least two weeks before the exam and the report has to be completed and sent to the teachers 2 days before the actual date of the exam. 

2) Oral - The oral examination focuses on the technical report and other general topics covered in the Course, in order to evaluate both the understanding of the fundamental principles of Machine Learning and the student's ability to apply these principles to real contexts.

The final mark will be calculated as average of the marks for the written and oral parts.


Suggested readings and bibliography


Lecture notes and scripts by the teachers.

Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd edition, O'Reilly Media Inc. ISBN: 9781492032649




The students with special needs and disabilities may find information on the follow web site:

Class scheduleV

Lessons: from 03/10/2022 to 22/12/2022

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