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CODE 106778
ACADEMIC YEAR 2023/2024
CREDITS
TEACHING LOCATION
  • GENOVA
MODULES Questo insegnamento è composto da:
TEACHING MATERIALS AULAWEB

OVERVIEW

The course presents, in module 1, the theoretical basis to formulate models based on experimental data. Basic knowledge will be provided on mathematical modelling, numerical calculus, regularization, numerical simulation of devices and systems- This knowledge will be exploited in module 2, that provides the foundation for the design and development of classification and regression algorithms. The student will be introduced to the basic concepts of machine learning (linear models, decision trees, ensemble learning, neural networks, etc.) through exercises using the main software libraries of the Python language (NumPy, Pandas, SciKitLearn and TensorFlow).

AIMS AND CONTENT

PREREQUISITES

Fundamental elements of mathematics, geometry, statistics and prgramming.

TEACHERS AND EXAM BOARD

Exam Board

ALBERTO OLIVERI (President)

RICCARDO BERTA (President Substitute)

EDOARDO RAGUSA (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

For module 1, The exam is oral and divided in two parts; each of them is referred to a half of the program. Each part assigns a maximum score of 15. The final score is the sum of the two partial scores.

For module 2, the exam is an oral examination on the theoretical topics covered during lectures. In particular, the student has to provide fluency in the description of the main concepts of machine learning.

The total grade is obtained by averaging the scores obtained in the two modules.

ASSESSMENT METHODS

During the oral exam, the teacher asks the student to illustrate some concepts learned in class. The student must demonstrate his/her knowledge and comprehension of the subject topics, by communicating properly, synthetically and with an adequate technical terminology. For each concept, the student has to present the definition, the conditions of applicability and pros/cons in relation to other approaches. During the examination, the teacher verifies that the concepts have been learned at a level of knowledge that allows the student to apply them in real cases.

The total grade is obtained by averaging the scores obtained in the two modules.

Exam schedule

Data Ora Luogo Degree type Note
22/12/2023 10:00 GENOVA Esame su appuntamento
30/01/2024 10:00 GENOVA Esame su appuntamento
16/02/2024 09:00 GENOVA Esame su appuntamento
31/05/2024 10:00 GENOVA Esame su appuntamento
27/06/2024 10:00 GENOVA Esame su appuntamento
31/07/2024 10:00 GENOVA Esame su appuntamento
13/09/2024 09:00 GENOVA Esame su appuntamento
13/09/2024 09:00 GENOVA Esame su appuntamento
12/01/2024 09:00 GENOVA Orale
30/01/2024 09:00 GENOVA Orale
16/02/2024 09:00 GENOVA Orale
05/06/2024 09:00 GENOVA Orale
28/06/2024 09:00 GENOVA Orale
17/07/2024 09:00 GENOVA Orale
05/09/2024 09:00 GENOVA Orale