AIMS AND CONTENT

AIMS AND LEARNING OUTCOMES

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.

TEACHING METHODS

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.

SYLLABUS/CONTENT

Part 1 - Fundamental algorithms and techniques
Introduction
Regression
Classification
Linear Models
Decision trees
Ensemble Learning
Dimensionality Reduction
Unsupervised algorithms

Part 2 - Neural Networks
Perceptron
MLP
Backpropagation
Convolutional networks
Advanced Vision

Part 3 - Deployment on embedded devices
Inference
Quantization
TensorFlow Lite and TensorFlow Micro
Deployment on NVidia Jetson platform
TinyML

RECOMMENDED READING/BIBLIOGRAPHY

1. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
2. Papers with Code
3. The Elements of Statistical Learning
4. Data Science from Scratch
5. Neural Networks for Machine Learning

TEACHERS AND EXAM BOARD

Exam Board

ALBERTO OLIVERI (President)

RICCARDO BERTA (President Substitute)

EDOARDO RAGUSA (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/8732/p/studenti-orario

Class schedule

L'orario di tutti gli insegnamenti è consultabile all'indirizzo EasyAcademy.

EXAMS

EXAM DESCRIPTION

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.

ASSESSMENT METHODS

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.

Exam schedule