ID:
000118RA
Duration (hours):
60
CFU:
6
SSD:
INFORMATICA
Located in:
PESCARA
Url:
ECONOMICS AND FINANCE/ECONOMIA E FINANZA Year: 2
Year:
2026
Course Catalogue:
Overview
Date/time interval
Secondo Semestre (12/02/2027 - 12/05/2027)
Syllabus
Course Objectives
The course aims to provide basic knowledge and skills on contemporary artificial intelligence, with particular attention to the ability to understand its general principles, use some operational tools and critically evaluate results, limitations and possible applications in economic, financial and business contexts.
In particular, this objective is related to the following expected learning outcomes.
Knowledge and understanding – the course aims to provide knowledge related to the fundamental concepts and basic language of artificial intelligence. Students are expected to understand the difference between artificial intelligence, machine learning, deep learning and generative artificial intelligence; the role of data in machine learning systems; the meaning of model, training, validation and test; and the main types of problems that can be addressed using AI tools, such as classification, forecasting, clustering, text analysis and content generation. Students are also expected to understand, at a conceptual and operational level, the general functioning and limitations of large language models.
Applying knowledge and understanding – the course aims to foster the development of the ability to apply the acquired knowledge to the analysis of simple economic, financial, business or administrative problems in which artificial intelligence tools may be used. This result is pursued through examples, guided exercises and applied activities. Students are expected to be able to identify possible AI use cases, distinguish between predictive, generative and analysis-support problems, use generative AI tools in a guided way, formulate effective prompts, critically interpret the answers obtained and take part in the design or implementation of simple workflows or prototypes assisted by LLMs.
Making judgements – the course aims to develop the ability to critically evaluate the use of artificial intelligence in relation to the problem addressed, the quality of the available data, the reliability of the results, possible errors and biases, and the role of human verification in analysis and decision-making processes.
Communication skills – students are expected to acquire the ability to describe the main concepts of artificial intelligence using appropriate language, clearly illustrating the general functioning of the tools used, their possible applications and their limitations.
Learning skills – the course aims to provide students with the foundations needed to continue the study of artificial intelligence independently, including through introductory texts, technical documentation, popular or scientific articles and commonly used software tools.
Course Prerequisites
No advanced prior knowledge of computer science or programming is required.
In order to facilitate attendance and learning, basic mathematical and statistical knowledge is useful, together with familiarity with the ordinary use of computers, spreadsheets and digital tools for managing texts, data and documents.
It may be useful, although not compulsory, to review elementary notions of descriptive statistics, basic probability, the use of spreadsheets and the management of files and tables before the beginning of the course.
It may also be useful to become familiar with the use of interactive notebooks and with basic Python functions for data manipulation. Any minimum programming notions needed for the applied activities will in any case be introduced during the course in a guided way, also through the use of programming tools assisted by language models.
As optional preparation, basic familiarity with generative artificial intelligence tools is also recommended, limited to the conscious use of prompts, the verification of responses and the management of simple writing, summarization and text analysis tasks.
Teaching Methods
Lectures.
Guided exercises.
Presentation and discussion of applied examples related to the use of artificial intelligence in economic, financial, business and administrative contexts.
Assisted practical activities aimed at using generative AI tools, analyzing data or documents and building simple workflows or prototypes in a guided way.
Assessment Methods
Knowledge and understanding – the learning outcomes of the course are assessed through an individual oral examination and/or the discussion of an applied assignment, according to the instructions provided by the teacher at the beginning of the course. The oral examination consists of questions related to the main topics of the programme, with the aim of assessing the student’s knowledge of the fundamental concepts of artificial intelligence and the ability to present them clearly and correctly.
The applied assignment, when provided, consists of the discussion of a simple artificial intelligence use case, developed individually or in groups according to the procedures indicated by the teacher. The assignment may concern, for example, the guided analysis of data or documents of economic, financial or business interest, the construction of a simple workflow assisted by LLMs, the use of generative AI tools for classification, summarization or analytical support activities, or the design of a small prototype.
Applying knowledge and understanding – during the examination, the teacher also assesses the student’s ability to apply the acquired knowledge to the analysis of concrete problems. In particular, students may be asked to describe a possible AI use case, evaluate the results produced by an AI system, discuss the limitations of a model, formulate prompts, interpret errors and biases, or critically present a simple prototype or workflow.
The final grade is expressed out of thirty. The assessment takes into account clarity of exposition, understanding of the fundamental concepts, the ability to apply the tools studied to simple cases, the ability to critically evaluate the results and limitations of AI systems, and the quality of the applied assignment, when provided.
Students with disabilities, specific learning disorders (SLDs), or other special educational needs may receive individualized arrangements during examinations, in compliance with current legislation and with the provisions of the University Service Charter (https://www.unich.it/sites/default/files/2024-02/carta_dei_servizi_0.pdf), subject to prior agreement with the course lecturer. To request compensatory measures and exemptions, students must contact the CON_TE_STO Service and follow the procedure set out in the Service Charter.
Students are invited to contact the lecturer during the teaching period, or in any case well in advance of the examination date, in order to obtain information about the examination procedures and the measures that may be granted.
For guidance and referral to the various services offered by the University, students may contact the Departmental Contact Person, Professor Domenico Raucci.
The applied assignment, when provided, consists of the discussion of a simple artificial intelligence use case, developed individually or in groups according to the procedures indicated by the teacher. The assignment may concern, for example, the guided analysis of data or documents of economic, financial or business interest, the construction of a simple workflow assisted by LLMs, the use of generative AI tools for classification, summarization or analytical support activities, or the design of a small prototype.
Applying knowledge and understanding – during the examination, the teacher also assesses the student’s ability to apply the acquired knowledge to the analysis of concrete problems. In particular, students may be asked to describe a possible AI use case, evaluate the results produced by an AI system, discuss the limitations of a model, formulate prompts, interpret errors and biases, or critically present a simple prototype or workflow.
The final grade is expressed out of thirty. The assessment takes into account clarity of exposition, understanding of the fundamental concepts, the ability to apply the tools studied to simple cases, the ability to critically evaluate the results and limitations of AI systems, and the quality of the applied assignment, when provided.
Students with disabilities, specific learning disorders (SLDs), or other special educational needs may receive individualized arrangements during examinations, in compliance with current legislation and with the provisions of the University Service Charter (https://www.unich.it/sites/default/files/2024-02/carta_dei_servizi_0.pdf), subject to prior agreement with the course lecturer. To request compensatory measures and exemptions, students must contact the CON_TE_STO Service and follow the procedure set out in the Service Charter.
Students are invited to contact the lecturer during the teaching period, or in any case well in advance of the examination date, in order to obtain information about the examination procedures and the measures that may be granted.
For guidance and referral to the various services offered by the University, students may contact the Departmental Contact Person, Professor Domenico Raucci.
Texts
The main reference material consists of lecture notes, slides, guided examples, notebooks and technical documentation made available or indicated by the teacher during the course.
During the course, articles, online resources, selected chapters from introductory texts and official documentation may be indicated, with reference to the artificial intelligence, machine learning, generative artificial intelligence and assisted programming tools used in lectures and applied activities.
The full study of specific textbooks is not required; the parts that are actually included in the course will be indicated by the teacher.
Contents
The course in Artificial Intelligence introduces the fundamental concepts, tools and main applications of contemporary artificial intelligence, with particular attention to economic, financial, business and administrative contexts.
In particular, the course covers: the general concepts of artificial intelligence, machine learning, deep learning and generative artificial intelligence; the role of data in the construction of predictive and generative models; the essential principles of model training, validation and evaluation; the use of large language models for writing, summarization, text analysis, classification, analytical support and document analysis; and the use of LLMs as tools to support programming and the guided construction of simple prototypes.
The course proposes examples and applied activities related to problems of economic, financial and business interest, such as the analysis of tabular data, the classification of information, the analysis of documents, the evaluation of simple predictive models and the use of generative tools to support analysis and the preparation of decisions.
The course also addresses the main limitations of artificial intelligence systems, with reference to the reliability of responses, data quality, the risk of errors and bias, the need for human verification and the conscious use of AI tools.
Course Language
Italian
More information
E-mail: maurizio.parton@unich.it.
WhatsApp or Telegram: 349-5323-199.
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