The objective of this course is three fold, first to provide the student with a knowledge about recommender systems. Then to present different succesful applications in the field of recommender systems, and finally different presentations of companies that use these technologies.
Santiago Seguí Mesquida (
Salvador Torra (
Generic Technical Competences
CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.
Technical Competences of each Specialization
CEA7 - Capability to understand the problems, and the solutions to problems in the professional practice of Artificial Intelligence application in business and industry environment.
CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
Analisis y sintesis
CT7 - Capability to analyze and solve complex technical problems.
Understand the general behaviour of the recommender systems
Understanding how recommender systems work to address the big amout of existing data.
Understanding the potential applications of recommender systems in the industry
Understanding the potential applications of AI in the business environment
Recommender Systems for industrial applications.
We will give an overview of different kinds of recommenders systems. From non-personalized to personalized recommeders.
We will explain how statistics that characterize the financial data affect the learning algorithms that are allow to infer the parameters of neural networks.
Recommender systems with Python
We will explain how recommender systems can be easily implemented and validated in Python.
Presentations of companies
Different companies will be invited to explain their applications in the field of AI
Properties of recommender systems
the student will work on the insight of the way that recommender systems work.
The teaching methodology is divided into three parts, the first is the exposure of theoretical concepts, the second in the visualization of the potential applications and thirdly presenting real case studies in business.
Método de evaluación
The evaluation of the seminar has three parts. A first case study the issues associated with 1-3 with a weight of 40%, a second case study topics related to 4-9 with 40% and finally a summary of the presentations of companies with 20%.