Introduction to Machine Learning (2235.YR.011241.1)

General information

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

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences	ENG

None

None

40% Class attendance
25% Assignments and readings
15% Preparation for exam
20% Preparation for group projects

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 will introduce of Machine Learning (ML), the field of AI that is being successfully applied to take automatic decisions and automate processes across many industries.

This course provides an initial understanding of ML, introducing its language and terminology, and discussing how and why it is a "horizontal enabling layer" for all areas in a company. The course also demonstrates how ML projects are structured and describes the basic methodologies for classification and clustering.

In other words, the aim is to provide the means for future professionals to reason using data and predictions and understand the associated opportunities.

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.

This is an introductory course with some hands-on practice. At the end of the
course, students should:
- 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 basic ML algorithms work, and in which situations they are useful.
- Start adopting a data-driven and evaluation-driven mindset.
- Practice machine learning hands-on, with real data, using assisted tools.
- Develop a critical mindset about AI, about what AI can and can't do, and about some issues of AI systems.

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. SUPERVISED LEARNING: CLASSIFICATION - Introduction to Supervised Learning and Classification - Decision Trees - Random forests - Logistic regression - Evaluation metrics in classification - Hands-on exercise

4. SUPERVISED LEARNING: REGRESSION Introduction to regression Error functions in regression Linear Regression Introduction to Neural networks Discussion of overfitting Hands-on practice

5. UNSUPERVISED LEARNING Introduction to Unsupervised Learning Clustering Introduction to Recommender Systems Business Case with BigML

6. 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.

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

8. MACHINE LEARNING FAILURES Discussion of failure cases of ML Debate

Class presentations
In-class demos and exercises
Practice with ML-assisted software
Individual Assignments
Exam

Non-technical readings:
- Prediction Machines: The Simple Economics of Artificial Intelligence, by Ajay Agrawal et al.
- Act Like a Scientist, by Tomke and Loveman, HBR 2022.

Optional technical books:
- Patter Recognition and Machine Learning by Christopher M. Bishop
- Artificial Intelligence by Russell and Norvig

Group	Teacher	Department
Year 1	Jose A. Rodriguez Serrano	Operaciones, Innovación y Data Sciences