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
Concepts of business administration
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
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,...).
Week 4
Unsupervised algorithms: clustering and profiling algorithms.
Week 5
Reinforcement Learning Algorithms: k-armed bandit, Policy Evaluation Algorithms.
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.