DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE - 2

Anno accademico
2023/2024 Programmi anni precedenti
Titolo corso in inglese
DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE - 2
Codice insegnamento
EM1405 (AF:449536 AR:257050)
Lingua di insegnamento
Inglese
Modalità
In presenza
Crediti formativi universitari
6 su 12 di DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE
Livello laurea
Laurea magistrale (DM270)
Settore scientifico disciplinare
ING-INF/05
Periodo
4° Periodo
Anno corso
1
Sede
VENEZIA
Spazio Moodle
Link allo spazio del corso
This course covers part of the "quantitative" aspects of the master program, and aims to introduce the student to the founding ideas of deep learning ad to offer a broad understanding of both its methodological and practical aspects.

More specifically, the goal of this course is to teach students the basic theoretical framework and to let them perform a practical, hands-on exploration of deep learning. Mathematical notation is paired with quantitative concepts via code snippets in order to help building practical intuition about the core ideas of machine learning and deep learning.
After the course, the students should have gained a solid understand of what deep learning is, when it’s applicable, and what its limitations are. They will be familiar with the standard
workflow for approaching and solving machine-learning problems, and they will know how to address commonly encountered issues.

Practice sessions will introduce to the use of Keras to tackle real-world problems ranging from computer vision to natural-language processing, image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.
Students should have achieved the learning outcomes of courses "Computer Programming And Data Management" and they should have attended proficiently the first module of this course (even without passing the corresponding exams).
* Supervised and Deep Learning overview and models
* Training and assessing models
* Neural Networks from scratch
* Deep Learning from scratch
* Convolutional Neural Networks
* Recurrent Neural Networks
* Introduction to Keras
* Applications with Computer Vision and Text Processing
Deep Learning from Scratch: Building with Python from First Principles 1st Edition, Seth Weidman, O'Reilly
Learning outcomes are verified by a project.

The project requires to design and perform a deep learning task by both conceive the general framework and by gathering and preparing data to train the system. The task should be selected with the aim of addressing a real-world problem. Results must be demonstrated with both a written report and a live presentation.
scritto e orale
Theoretical and practical lectures.
Exercise lectures.
Programma definitivo.
Data ultima modifica programma: 08/05/2023