- Docente: Stefano Lodi
- Credits: 4
- Language: Italian
- Moduli: Stefano Lodi (Modulo 1) Tommaso Pirini (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Rimini
- Corso: First cycle degree programme (L) in Finance, Insurance and Business (cod. 8872)
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from Feb 22, 2023 to Mar 22, 2023
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from Feb 23, 2023 to Mar 22, 2023
Learning outcomes
By the end of the course, the students know the basics of programming in Python and the usage of the main Python libraries for statistical and scientific computation, and have the skills to perform analyses of case studies using the Python language.
Course contents
Module 1
Supervised classification models: neural networks. Processing examples from the Machine Learning literature.
Module 2
The Python language. Expressions, tuples, lists, comprehensions, sets, dictionaries. Repetitive and branching instructions. File read and write operations. Functions. Package Numpy, matplotlib and scipy.
Readings/Bibliography
Course slides are available on Virtuale.
Recommended readings:
Parker, J. R. (2016). Python: An Introduction to Programming. Mercury Learning & Information.
Zhang, Y. (2015). An Introduction to Python and Computer Programming. Senegal: Springer Singapore. Warning:
this book is based on Python v. 2, which slightly differs from Python v. 3, which is used in the course.
Both books are free to download (using student institutional credentials) E-books searchable in
SBA | Online resources | E-books | Ricerca un e-book nel Catalogo A-Link
Teaching methods
NOTE: As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online.
The lessons of the course are held in laboratory. Frontal lessons and exercise alternate.
Assessment methods
Oral examination (duration: about 15m). The grade is between 0 and 30. The student must demonstrate: thorough knowledge of the instructions of the Python language; skills in Python programming, applied both to general purpose algorithms and analysis cases in Machine Learning.
Attendance does not contribute to the assessment.
Teaching tools
Slide presentations, laboratory of PCs with access to Windows q0 virtual machines with a Python distribution for Machine Learning installed.
Office hours
See the website of Stefano Lodi
See the website of Tommaso Pirini
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.