Intelligent Data Analysis Applications in Business

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

    1. Understand the general behaviour of the recommender systems
      Related competences: CT4, CT7,
    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: CG3, CEP3,
    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
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Report on a potentially novel use of AI technologies


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

    Synthesis company presentations

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

    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.