ARTIFICIAL INTELLIGENCE FOR DECISION-MAKING PROCESSES
Instructional goals
The course aims to introduce the dynamics and megatrends of Artificial Intelligence technologies and to analyse the impacts they will have on socio-economic processes, especially those supporting information processing in general and decision-making in particular. During the course, concrete cases of projects in which AI-based technologies have innovated products and services offered by companies in a sometimes very strong way will be discussed.
Intended learning outcomes
Basic knowledge of the main predictive and generative AI technologies. Application areas of these technologies to real-world business decision-making processes (marketing, risk, finance, etc.). Ability to evaluate investments in AI-driven technologies in terms of economic and social impact, with the goal of integrating them into decision-making and/or production processes.
Course Contents
Introduction to Artificial Intelligence technologies. Brief historical background and future trends. Main types of AI algorithms and their possible applications on decision-making processes.
Reference Books
Lecturer's Handouts
and
Melanie Mitchell, "Artificial Intelligence: A Guide for Thinking Humans", (2019)
Teaching Methods
Lectures and case studies
Assessment Method
Oral examination
Thesis assignment criteria
None in particular
Week 1
Introduction and history of IA (1)
Week 2
Introduction and history of IA (2)
Ongoing technological trends.
Week 3
Types of AI algorithms
Supervised Algorithms: algorithms predicting and classifying all types of data (sounds, images, texts,...). Unsupervised algorithms: clustering and profiling algorithms.
Week 4
Reinforcement Learning Algorithms: k-armed bandit, Policy Evaluation Algorithms.
Week 5
Large Language Models (LLM), Generative AI. Introduction to the approach to artificial agents.
Week 6
Case studies of the application of algorithms to decision-making processes. Areas of application.
Week 7
Presentation of application cases to Marketing: behaviour-based profiling, segmentation and extrapolation algorithms through direct and indirect analysis.
Week 8
Presentation of application cases to risk management: behaviour-based profiling, segmentation and extrapolation algorithms through direct and indirect analysis.
Week 9
Presentation of application cases to financial markets: algorithms for analysing the financial statements of listed companies, analysis of price trends (1/3)
Week 10
Presentation of application cases to financial markets: algorithms for analysing the financial statements of listed companies, analysis of price trends (2/3).
Week 11
Presentation of application cases to financial markets: algorithms for analysing the financial statements of listed companies, analysis of price trends (3/3).
Week 12
Presentation of application cases in diagnostic screening: profiling, segmentation and extrapolation algorithms based on clinical and behavioural data.