Intelligent Data Analysis Applications in Business

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Credits
2
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
UB
Mail
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.

Teachers

Person in charge

  • Jerónimo Hernández González ( )

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

  1. Understand the general behaviour of the recommender systems
    Related competences: CT7, CT4,
  2. Understanding how recommender systems work to address the big amout of existing data.
    Related competences: CEA7, CEP3,
  3. Understanding the potential applications of recommender systems in the industry
    Related competences: CEP3, CG3,
  4. Understanding the potential applications of AI in the business environment
    Related competences: CEP5,

Contents

  1. 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.
  2. Real experiences of AI applications in the industry
    Different companies will be invited to explain their applications in the field of AI

Activities

Activity Evaluation act


Notebook solution

Students will solve a (set of) notebook(s) proposed at the lab session
Objectives: 1 2
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


Objectives: 2 3
Week: 1
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Synthesis company presentations

Perform a synthesis of the contributions by the companies
Objectives: 4
Week: 1
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Introduction to recommender systems

The student will work on the insight about recommender systems.
Objectives: 1 2
Contents:
Theory
4h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
0h

Real experiences of AI applications in the industry

The student will observe business practice
Objectives: 4
Contents:
Theory
0h
Problems
10h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

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%).

Bibliography

Basic:

Previous capacities

Interest in business and financial applications from the perspective of AI.

Addendum

Contents

No significant changes are expected.

Teaching methodology

Beyond the possible adaptation of the course to a mixed (presential-virtual) methodology, no other significant changes are expected.

Evaluation methodology

No significant changes are expected.

Contingency plan

In case of complete lockdown, lessons will be fully virtual-synchronous.