Introduction to Machine Learning (19CI11037)

General information

Type:	OP	Curs:	1	Period:	S semester
ECTS Credits:	2 ECTS

Teaching Staff:

Group	Teacher	Department	Language

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Artificial Intelligence (AI) is transforming our society in a new and unprecedented industrial revolution. AI is impacting organisations 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.

At the end of the course, students should:
- 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.
- Have the necessary skillset to lead and manage company transformations based on AI technologies.
- Be able to have an informed discussion about any general topic involving AI technologies including ethics, legislation, education and employment.

1. AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE This introductory session gives the basic concepts of AI and guides you through its evolution to understand how it is applied to transform industries and businesses. ¿ What is Artificial Intelligence (AI)? ¿ The AI evolution and its future ¿ Artificial General Intelligence versus Applied AI ¿ The impact of AI in businesses ¿ The impact of AI in society ¿ Examples of AI systems used in our daily lives

2. MACHINE LEARNING LIFE CYCLE Machine Learning (ML) is the most successfully applied AI technolgogy nowadays. ML is about analysing large data sets to discover knowledge and insights on organizations and customers. Those insights are encoded into models that are capable of predicting outcomes from data. ¿ Machine Learning Introduction ¿ The Machine Learning Life Cycle ¿ Supervised and Unsupervised Learning Algorithms ¿ Practical example using BigML

3. LOGISTIC REGRESSION ¿ Logistic Regression ¿ Business Case with BigML

4. DECISION TREES ¿ Decision Trees ¿ Business Case with BigML

5. RANDOM FORREST ¿ Random Forrest ¿ Business Case with BigML

6. UNSUPERVISED LEARNING ¿ Anomaly Detection ¿ Clustering ¿ Business Case with BigML

7. RECOMMENDER SYSTEMS AND PERSONALIZATION (I) Recommender Systems are boradly used in many industries. Companies such as Netflix, Amazon, Spotify and Facebook have built their core business around Recommendation and Personalization technologies. ¿ Why do we need Recommender Systems? ¿ What is a Recommender System? ¿ Collaborative-based Filtering ¿ Examples

8. RECOMMENDER SYSTEMS AND PERSONALIZATION (II) ¿ Content-based Filtering ¿ Examples

9. EXAM AND PROTOTYPE IMPLEMENTATION WITH BIGML Individual Evaluation session. Students will be required to solve a business case with BigML and go through a set of questions on the concepts learned in the course.

10. FINAL PROJECT PRESENTATION Session for group presentations of their final project.

- Predictive Analytics by Eric Siegel
- Thinking Fast and Slow by Daniel Kahneman
- Homo Deus by Yuval Noah Harari
- Patter Recognition and Machine Learning by Christopher M. Bishop
- Machine Learning course by Coursera (University of Minnesota)

Group	Teacher	Department