Contactar esade

Artificial Intelligence and Machine Learning (18BBA11030)

General information

Type:

OP

Curs:

2,3

Period:

S semester

ECTS Credits:

4 ECTS

Teaching Staff:

Group Teacher Department Language
Marc Torrens Arnal Operaciones, Innovación y Data Sciences ENG

Prerequisites

None

Previous Knowledge

Basic computer science knowledge.

Workload distribution

Workload distribution:
Lectures: 15 hours
Participatory sessions: 12 hours
Independent study: 30 hours

COURSE CONTRIBUTION TO PROGRAM

Artificial Intelligence (AI) is transforming our society in a new and unprecedented industrial revolution. AI is impacting organizations at their core, reshaping business models and enriching people's daily lives. This course provides an overview of AI technologies, and explains how they can be used in practice. Specific focus will be given to Machine Learning (ML) and Recommender Systems that are being successfully applied to disrupt many industries. This course is designed to acquire a deep understanding of the main AI techniques from a business point of view.
The course uses a mix of engaging lectures and hands-on activities on practical business cases. This course also involves using ML tools to prototype real cases. At the end of the course, students will be able to understand AI main technologies, identify business opportunities based on ML and Recommender Systems, and prototype real business cases.

Course Learning Objectives

The aim of this course is for students to learn how to:
- Understand the current and future impact of AI in our society and businesses.
- Be able to identify AI-based opportunities within organizations. This implies to identify (big) data value and know how to apply AI to create new business models with innovative products and services.
- Understand the different available AI technologies and how to apply them in different contexts.
- Be acknowledgeable on available platforms and tools to successfully apply AI in companies.
- Know how to build AI prototypes based existing platforms such as BigML to validate ideas.

This course is structured in 5 main topics. Most of the topics includes a lecture and a discussion on a practical business case or an AI-based prototyping exercise.

Assessment criteria

30% Prototype implementation (individual)

20% Class Participation

50% Term paper (group)

Timetable and sections

Group Teacher Department
Marc Torrens Arnal Operaciones, Innovación y Data Sciences

Timetable