The objective of this seminar is two-fold: first, to provide the student with basic notions about recommender systems, and to get to know about uses of AI techniques for solving real-world applications in the industry.
Weekly hours
Theory
4
Problems
10
Laboratory
4
Guided learning
0
Autonomous learning
32
Competences
Generic Technical Competences
Generic
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
Academic
CEA7 - Capability to understand the problems, and the solutions to problems in the professional practice of Artificial Intelligence application in business and industry environment.
Professional
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.
Transversal Competences
Information literacy
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.
Objectives
Understand the general behaviour of the recommender systems
Related competences:
CT7,
CT4,
Understanding how recommender systems work to address the big amout of existing data.
Related competences:
CEA7,
CEP3,
Understanding the potential applications of recommender systems in the industry
Related competences:
CEP3,
CG3,
Understanding the potential applications of AI in the business environment
Related competences:
CEP5,
Contents
Recommender Systems for industrial applications.
We will give an overview of different kinds of recommenders systems, uses and evaluation.
Collaborative Filtering: we will explain how Collaborative Filtering works, and how we can use other users' information for making recommendations.
Coding a recommender system: we will explain how recommender systems can be easily implemented and validated in Python.
Real experiences of AI applications in the industry
Different companies will be invited to explain their applications in the field of AI
Activities
ActivityEvaluation act
Notebook solution
Students will solve a (set of) notebook(s) proposed at the lab session Objectives:12 Week:
1 Type:
lab exam
Theory
0h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
10h
Report on a potentially novel use of AI technologies
During this seminar, different methodologies will be followed. In a master class, basic theoretical concepts will be explained. A guided lab session will be used for putting those concepts in practice. Finally, a set of real case studies in business will be presented.
Evaluation methodology
The evaluation of the seminar has three parts: Firstly, a report on a potentially novel use of artificial intelligence technologies (30%); secondly, a practical notebook (30%); and, finally, a summary of the AI technologies presented by the companies (40%).