Artificial Intelligence & Machine Learning (2235.YR.000563.1)

General information

Type:	OPT	Curs:	3,4	Period:	S semester
ECTS Credits:	4 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 3	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences	ENG

Group	Teacher	Department	Language
Year 4	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences	ENG

None

Basic computer science knowledge.

4 ECTS = 100 hours of work

Lectures: 30%
Independent study / assignments / readings: 50 %
Preparation for group project: 20%

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.

- Understand the current and future impact of AI in our society and businesses.
- Understand basic terminology and concepts about Machine Learning and AI
- Broadly understand the general methodology of using ML in a business opportunity
- Get to know and understand how some supervised and unsupervised learning algorithms, and in which situations they are useful.
- Start adopting a data-driven and evaluation-driven mindset.
- Practice hands-on with real data and machine learning software.
- Develop a critical mindset about AI, about what AI can and can't do, and about some issues of AI systems.

Most of the topics includes a lecture and a discussion on a practical business case or an AI-based prototyping exercise and post-lecture assignments.

1. Introduction to AI

2. Introduction to Machine Learning

3. Machine Learning Cycle

4. Supervised learning: Classification

5. Supervised Learning: Regression

6. Unsupervised Learning: Clustering

7. Business Applications of AI

8. Introduction to Recommender Systems

Lectures consist of theoretical explanations, in-class practical demonstrations and activities, student participation and debates. Students will also develop a group project to practice the learned concepts in simulated business conditions.

- Ajay Agrawal et al., Prediction Machines

Group	Teacher	Department
Year 3	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences

Group	Teacher	Department
Year 4	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences