Teaching plan for the course unit

 

 

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General information

 

Course unit name: Intelligent Data Analysis Applications in Business

Course unit code: 573298

Academic year: 2021-2022

Coordinator: Jerónimo Hernández González

Department: Department of Mathematics and Computer Science

Credits: 2

Single program: S

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Estimated learning time

Total number of hours 50

 

Face-to-face and/or online activities

20

(Due to the Covid-19 restrictions, we expect to have 50%-75% of in-person activities.)

Independent learning

30

 

 

Teaching blocks

 

1. Recommender Systems for industrial applications

2. Real experiences of AI applications in the industry

 

 

Teaching methods and general organization

 

Teaching will follow a face-to-face (in-person), virtual (online) or mixed model according to the instructions of the competent authorities. In principle, we expect to follow the mixed teaching model for the 2021-22 academic year. 

* In case of in-person teaching:

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.

* In case of mixed teaching required by the health situation (this is the expected model):

If the health situation allows it and the necessary conditions are met, we expect to have between 50% and 70% of in-person activities. In general, when having an occupancy rate of 50%, students will be invited to follow in-person the presentation in alternate days and will follow them on streaming otherwise.

* In case on-line teaching is required by the health situation:

The time ranges of mixed teaching are maintained but all teaching will be carried out in an online format, prioritizing synchronous sessions. 

As far as possible, the gender perspective will be incorporated in the development of the subject.

 

 

Official assessment of learning outcomes

 

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%).

 

Examination-based assessment

The seminar is expected to be evaluated using a practical notebook (30%) and a project involving a potentially real application of AI (70%).