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DATA SCIENCE FOR CHEMISTS FROM MODELLING TO MACHINE LEARNING
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DATA SCIENCE FOR CHEMISTS FROM MODELLING TO MACHINE LEARNING
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Anno accademico 2023/2024
- Codice attività didattica
- CHI0191
- Docenti
- Marta Corno (Titolare degli insegnamenti)
Jacques Kontak Desmarais (Titolare degli insegnamenti) - Anno
- 1° anno
- Periodo
- Primo periodo
- Tipologia
- Affine o integrativo
- Crediti/Valenza
- 6
- SSD attività didattica
- CHIM/02 - chimica fisica
- Erogazione
- Tradizionale
- Lingua
- Inglese
- Frequenza
- Obbligatoria
- Tipologia esame
- Scritto più orale obbligatorio
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Sommario insegnamento
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Obiettivi formativi
The course outlines concepts of computer modelling and machine learning for the physical chemist. The student will learn about supervised and unsupervised learning algorithms for data analysis, as well as methods of classical and quantum-mechanical modelling in physical chemistry. The algorithms will be applied with hands-on sessions, employing the python programming language.
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Risultati dell'apprendimento attesi
- Familiarity with the jargon and array of algorithms for machine learning and modelling in physical chemistry
- Basic knowledge of the python programming language and scientific computing
- Sensibility to the pros and cons of different modelling and machine learning strategies- Oggetto:
Programma
Introduction (where do data come from?; what is Machine Learning?)
Modelling (What is so difficult?; The Schrodinger equation and the electron correlation problem)
Machine Learning (Models of Supervised and Unsupervised data classification and regression; Parametrizing the model: analytical solutions, the least-squares problem, gradient-based numerical optimizations)
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Modalità di insegnamento
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 48 hours]
All classes will take place face-to-face (Lecture Hall TBD)
All didactic material will be published on the campusnet 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.- Oggetto:
Modalità di verifica dell'apprendimento
Written and oral exams (both mandatory).
The exam consists in:
1) Written - The written test on general topics covered in the course2) Oral - The oral examination focuses on general topics covered in the course, in order to assess the students understanding of approaches for modelling and machine learning through scientific computing.
The final mark will be calculated as an average of the marks for the written and oral parts.
Testi consigliati e bibliografia
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SHARED GOOGLEDRIVE FOLDER WITH LECTURES PDFS (prof. Corno):
https://drive.google.com/open?id=1Bd8vpFWiwkOc2QdskqDoWjAONFpbOETW&usp=drive_fs
Shared folder with Lecture PDFs (Desmarais):
https://drive.google.com/drive/folders/1aMlPsuTDNca7lwmaonidU5AMKJEK9zPa?usp=sharing
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Note
Lecture notes and scripts by the teachers.
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