|MODULES||This unit is composed by:|
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).
Fundamental elements of mathematics, geometry, statistics and prgramming.
Office hours: by appointment
All class schedules are posted on the EasyAcademy portal.
For module 1, the exam is divided in two parts; each of them is referred to a half of the program. Each part consists in the explanation of one topic chosen by the student and one topic chosen by the teacher. The total score is 30.
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.
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.