OVERVIEW

The amount of data in industrial applications is exponentially growing. Efficient and sophisticated tools are needed to manage it.
This course presents the most recent methods of data processing and content generation.

AIMS AND CONTENT

LEARNING OUTCOMES

Develop the basic skills for extracting knowledge and knowledge from large data sets, in particular by forming an understanding of the value of data mining in solving real-world problems understanding of foundational concepts underlying data mining understanding of algorithms commonly used in data mining tools ability to apply data mining tools to real-world problems

AIMS AND LEARNING OUTCOMES

The student will be able manage a data analysis problem in a group, generate a report and thus understand a business phenomenon.

PREREQUISITES

Coding (Matlab/Python/R), linear algebra, probability and statistics, Machine Learning

TEACHING METHODS

In-class lectures and laboratory sessions with exercises in Matlab/Python/R

SYLLABUS/CONTENT

1 Introduction to data science in the business framework
2 Classic machine learning methods for classification and regression problems
3 Data-centric approaches to improve real-world datasets
4 Methods of optimal selection of the features
5 Neural networks and deep learning
6 Convolutional Networks (CNN)
7 Applications to computer vision
8 Definition of Predictive AI and Generative AI
9 Predictive AI: from feature engineering to the definition of Embedding. Projection into the space of embeddings, clustering and classification algorithms
10 Introduction to Recurrent Neural Networks (RNN)
11 History and development of Generative AI
12 Introduction to GANs (Generative Adversarial Networks)
13 The concept of Transformer and (Self-)Attention
14 Applications of Generative AI
15 Creative text generation
16 Image generation and generative art
17 Applications in medicine, finance, and other industries

RECOMMENDED READING/BIBLIOGRAPHY

  "Deep Learning" by Ian Goodfellow, Yoshua Bengio, Aaron Courville.
  "Generative Deep Learning" by David Foster.
  "Probabilistic Machine Learning" by K,P.Murphy (Volume 1)
  "An Introduction to Statistical Learning with Applications in Python", by G.James et al.
  Scientific papers and online resources for selected applications

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCO PORRO (President)

FABRIZIO MALFANTI

EVA RICCOMAGNO (Substitute)

SARA SOMMARIVA (Substitute)

LESSONS

LESSONS START

According to official academic calendar

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

Exam schedule

FURTHER INFORMATION

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.