Introduction to Machine Learning

Recommended Prerequisites

Probabilities and Statistics, Linear Algebra, Calculus, Programming

Teaching Methods

The course consists of theoretical classes (T) that provide a detailed presentation of concepts, principles, and fundamental theories using audiovisual aids. Basic practical exercises are solved during these classes to demonstrate the practical relevance of the subject and illustrate its application to real-life scenarios. Additionally, practical laboratory classes (PL) are conducted, where students work on exercises that require the integration of various theoretical concepts and foster critical reasoning skills.

The evaluation process encompasses all the topics covered in the course and focuses on assessing understanding of the fundamental theoretical concepts as well as the ability to solve complex problems.

Learning Outcomes

The course aims to introduce the machine learning area, providing students with an overview of its area, methodological principles, challenges, and main applications. The objective is to familiarize students with the basic algorithms used in a data analysis pipeline, with a particular focus on data preparation, feature extraction, dimensionality reduction, machine learning models, and their validation. At the end, it is expected that the student will be capable of identifying and designing pipelines, selecting the most suitable computational learning methodologies, and experimentally and formally validating the best algorithmic solution for a specific task.. The course also encourages self-directed learning, group work, interpersonal relationships, and oral and written communication skills.

Work Placement(s)

No

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

Peter Flach, Machine Learning: the art and science of algorithms that make sense of data, Cambridge University Press, 2012.

 

Ilyas and X. Chu, Data Cleaning, ACM, 2019.

 

P Duboue, The art of Feature Enguineering: Essentials for Machine Learning,.

 

C. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, 2016.

 

A. Géron, Hand-on Machine Learning with Scikit-Learn, Keras & TensorFlow, O'Reilly.

 

Introduction to Machine Learning with Python, Andreas C. Muller and Sarah Guido, O'Reilly, 2017.

 

García, Luengo & Herrera (2015). "Data Preprocessing in Data Mining". Springer.

 

Nixon & Aguado (2008). "Feature Extraction & Image Processing". Academic Press.