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Oggetto:

Machine Learning and its Application to Chemistry and Materials Science

Oggetto:

Machine Learning and its Application to Chemistry and Materials Science

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Academic year 2021/2022

Course ID
CHI0168
Teachers
Prof. Alessandro Erba (Lecturer)
Dott. Eugenio Alladio (Lecturer)
Dott. Lorenzo Mino (Lecturer)
Degree course
Materials Science
Year
2nd year
Teaching period
First semester
Type
Optional
Credits/Recognition
4
Course disciplinary sector (SSD)
CHIM/01 - chimica analitica
CHIM/02 - chimica fisica
Delivery
Class Lecture + Lab Practicals
Language
English
Attendance
Optional
Type of examination
Oral + Lab Reports
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Sommario del corso

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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).

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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.
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Program

  • 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)
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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 (in Aula D1, at the 5-th floor of the building in via Giuria 9) and simultaneously streamed on-line via WebEx on the personal rooms of the teachers:

https://unito.webex.com/meet/alessandro.erba (Lessons 1-4)

https://unito.webex.com/meet/eugenio.alladio (Lessons 5-12)

https://unito.webex.com/meet/lorenzo.mino (Lessons 13-16)

In case of persistence of health emergency due to COVID-19, the course will be delivered remotely. The course will be organized into theoretical lessons (asynchronous), activities to be carried out online on the Moodle platform, exercises and periodic interviews with students, using the WebEx platform.

All didactic material will be published and /or available on the Moodle platform: asynchronous
lessons and synchronous exercises (recorded), examples of solved exercises and tests for training and self-assessment.

The Course has a strong hands-on character so all students will need to have either a laptop (for face-to-face attendance) or a PC (for on-line attendance).

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. 

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Learning assessment methods

Written and oral exams (both mandatory).

The exam consists in:

1) Written - The written test consists of 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. The mark, expressed in thirtieths, is valid until the beginning of the following academic year. The positive outcome (≥ 18/30) of this test allows access to the oral test.

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.

ON LINE EXAMS: In case of persistence of the health emergency due to COVID-19, the oral examination will be carried out remotely. It will consist of an oral interview via Web-Ex, according to the Rectoral Decree n.1097 / 2020.

Suggested readings and bibliography

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Lecture notes and scripts by the teachers



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Class scheduleV

Lessons: dal 20/10/2021 to 15/12/2021

Notes: Classes will take place in the following days/classrooms:

20 October 2021, 11.00-13.00 in Aula D1 (5-th floor of via Giuria 9)
22 October 2021, 11.00-13.00 in Aula D1 (5-th floor of via Giuria 9)
27 October 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
29 October 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
03 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
05 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
10 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
12 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
17 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
19 November 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
24 November 2021, 11.00-13.00 in Aula DIAGONALE (2-nd floor of via Giuria 7)
26 November 2021, 11.00-13.00 in Aula DIAGONALE (2-nd floor of via Giuria 7)
01 December 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
03 December 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
10 December 2021, 11.00-13.00 in Aula DISEGNO (2-nd floor of via Giuria 7)
15 December 2021, 11.00-13.00 in Aula D1 (5-th floor of via Giuria 9)

Enroll
  • Closed
    Enrollment opening date
    01/07/2021 at 00:00
    Enrollment closing date
    30/11/2021 at 23:55
    Maximum number of students
    25 (Once this number of students is reached, enrollment will no longer be permitted!)
    Oggetto:
    Last update: 04/04/2022 09:54
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