MACHINE LEARNING

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

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General Module

Course Sheet

Course code:

000913LM1

Academic Year of enrolment:

2021

Year of supply:

2023/2024

Type of Course:

Elective Course

Degree:

Course of 1st Cycle Degree in ECONOMICS AND BUSINESS INFORMATICS

Disciplinary Sector:

Computer Science

Is part of:

ARTIFICIAL INTELLIGENCE

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

SCOZZARI Francesca

Cycle:

First semester

Hours of classroom activity:

Individual study hours:

Prerequisites:

None.

Objectives

Students will be able to correctly formulate and solve machine learning problems.
Learning outcomes

Knowledge and understanding

- Provide an overview of the main techniques of machine learning: classification, regression, supervised and unsupervised learning.
- Knowledge of the Python programming language.

Applying knowledge and understanding
- Solve machine learning problems starting from various data sets, by applying the techniques studied.
- Use of the Python programming language.

Contents

The aim of the course is to present machine learning techniques in the field of artificial intelligence, such as neural networks and deep learning, discussing their properties and applicability.

Extended Syllabus

- Introduction to the Python programming language
- Machine learning
- Introduction to Machine learning
- Neural networks
- Deep learning
- Feedforward networks
- Convolutional networks
- Graph Neural Network (GNN)
- Generative Adversarial Network (GAN)

Recommended Bibliography

Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach, Global Editio. 4th Edition. Pearson. 2022.

François Chollet. Deep Learning with Python, Second Edition. Manning Publications. 2021.

Teaching Methods

Lectures.
Laboratory exercises in Python using various libraries for machine learning.

Evaluation methods

Verification of learning:

Mandatory written/practical examination and optional oral examination.

Other courses

Course year: 1

FOUNDATIONS OF COMPUTER SCIENCE
9 Credits
PROGRAMMING AND ALGORITHMS 1
9 Credits
BUSINESS ADMINISTRATION
9 Credits - Basic
CALCULUS
9 Credits - Basic
MICROECONOMICS
9 Credits - Basic
PRIVATE LAW AND NEW TECHNOLOGY LAW
9 Credits - Basic
CERTIFICATE OF COMPETENCY IN TECHNICAL-SCIENTIFIC ENGLISH
6 Credits - Language/Final Examination

Course year: 2

DATABASES
6 Credits
PROGRAMMING AND ALGORITHMS 2
9 Credits
WEB PROGRAMMING
6 Credits
COMMERCIAL AND ECONOMIC LAW
9 Credits - Characterising
ECONOMIC GEOGRAPHY
6 Credits - Characterising
GAME THEORY
6 Credits - Characterising
INTERNET ECONOMICS
9 Credits - Characterising
LABOUR LAW
9 Credits - Characterising
MACROECONOMICS
9 Credits - Characterising
STATISTICS
9 Credits - Characterising

Course year: 3

ARTIFICIAL INTELLIGENCE
6 Credits
DATA MINING
6 Credits - Characterising
ECONOMETRICS
9 Credits - Characterising
FINANCIAL REPORTING
9 Credits - Characterising
ADVANCED PROGRAMMING
6 Credits - Related/Supplementary
INTERNET AND NETWORKING
6 Credits - Related/Supplementary
MOBILE SYSTEMS
6 Credits - Related/Supplementary
REINFORCEMENT LEARNING IN ARTIFICIAL INTELLIGENCE
6 Credits - Related/Supplementary
DEEPENING OF STATISTICS
9 Credits - Elective Course
ELECTIVE COURSES
18 Credits - Elective Course
IN-DEPTH STATISTICS
6 Credits - Elective Course
IN-DEPTH STATISTICS
3 Credits - Elective Course
FINAL EXAMINATION
3 Credits - Language/Final Examination
PROFESSIONAL SEMINARS
6 Credits

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MACHINE LEARNING