|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/01|
|MODULES||This unit is a module of:|
The aim of the course is to provide the basis for the design and development of classification and regression software algorithms. The student is introduced to different concepts of machine learning (linear models, decision trees, ensemble learning, artificial neural networks, etc.) and supported through extensive exercises during lectures exploiting several software library in Python (NumPy, Pandas, SciKitLearn e TensorFlow). The last part of the course will be focused on the model deployment on embedded systems.
The course is composed of a set of frontal lessons and a set of practice sessions. During the frontal lesson, the teacher presents the topics providing also examples of live code that are tested on a Jupyter notebook. Students can use their own laptops during the lecture in order to reproduce what is proposed by the teacher. During the practice sessions, the students have to face up with real problems that they should solve by applying the techniques learnied during the lectures.
Part 1 - Fundamental algorithms and techniques
Part 2 - Neural Networks
Part 3 - Deployment on embedded devices
TensorFlow Lite and TensorFlow Micro
Deployment on NVidia Jetson platform
All class schedules are posted on the EasyAcademy portal.
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.
During the oral exam, the teacher asks the student to illustrate some concepts learned in class. 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.