BUSINESS AND MARKETING ANALYTICS

The main objective of this course is to give a basic introduction to artificial intelligence (AI) and its subsets, machine learning (ML). Through Python programming, the students are given a practical understanding of the methods being taught, mainly through implementing several methods. The course covers supervised classification based on e.g., decision trees, unsupervised learning (clustering), and the design of experiments and evaluation. Students also receive an introduction to fundamental problems and ethical questions related to ML/AI, as well as the implications of that. A significant part of the course will be devoted to using these tools to solve various real-life problems in the Marketing domain.

Basic knowledge of statistics and basic understanding of computer science.

Basic introductory-level knowledge in using Machine Learning and Deep Learning algorithms for solving real-world problems in marketing, financial, capital market scenarios and recommender system.

The course focuses on theoretical foundations, techniques, methodologies, and applications of artificial intelligence, specifically machine learning, to contribute to the dissemination and advancement of knowledge and skills on this subject and its applications, thereby promoting technological innovation and fostering economic and social development. Specifically, the course will cover the following topics: - Introduction to Artificial Intelligence, Big Data, Machine Learning and Deep Learning. - Introduce basic theoretical knowledge about Machine Learning algorithms. - Solving real cases in the Marketing domain: applications and use-cases of the studied Machine Learning models. - Basic knowledge about ethical AI framework: robustness, interpretability, fairness and accountability. - Use of Machine Learning libraries within the python programming language. - Use cases and applications of the topics covered using the Python Programming Language.

Agresti, C. Franklin (2014) Statistics - The Art & Science of Learning from Data (3th edition –International Edition), Pearson, Essex, England. Pattern Recognition and Machine Learning (Information Science and Statistics) 0387310738 I Springer-Verlag A Christopher M. Bishop 2006 Marcos Lopez de Prado Advances in Financial Machine Learning Wiley , New Jersey, 2018

- Lectures (50%) - Lab sessions addressed for realizing the final Project work (40%) - Seminars with companies (10%) - These activities will be planned to allow students to practically realize the solutions to the problems outlined during the theoretical lectures using Python coding. Supporting teaching materials (handouts, video tutorials, datasets, exercises and demos) will be provided.

Attending Students Evaluation Competencies will be assessed by 70% through the evaluation of a Project Work (continuous assessment) done by a team of up to 3 people. The Project Work assignment will be conducted during the 10th week of class. The other 30% of the assessment (final examination) will be relative to each student and will focus on discussing the project work and theoretical questions of the topics covered during the course. Non-Attending Students Evaluation Competencies will be assessed by 70% through the evaluation of a Project Work (continuous assessment) done by a team of up to 3 people (non-attending students). The Project Work assignment will be performed by sending a mail to the teacher. The other 30% of the assessment (final examination) will be relative to each student and will focus on discussing the project work and theoretical questions of the topics covered during the course.

- Evaluation of project work (Project Work): 70% - Individual discussion of the final project and individual questions on the topics covered during the course: 30%