This course will introduce you to different advanced techniques within the area of unsupervised machine learning. Topics include dimensionality reduction techniques, clustering algorithms for structured and unstructured data, frequent patterns, and unsupervised learning based on Deep Learning techniques (Autoencoders, GANs, self supervised and contrastive learning).
Person in charge
Javier Béjar Alonso (
Generic Technical Competences
CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.
CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.
Technical Competences of each Specialization
CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
Sustainability and social commitment
CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.
CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
To known and use advanced unsupervised machine learning techniques for application on all the domains of engineering and science
Data Mining, a global perspective
Brief introduction to what is Data Mining and Knowledge Discovery, the areas they are related to and the different techniques involved
Pre-processing and unsupervised data transformation
This topic will include different algorithms for unsupervised data preprocessing such as data normalization, discretization, outliers detection, dimensionality reduction and feature extraction (PCA, ICA, SVD, linear and non linear multidimensional scalling and non negative matrix factorization)
Unsupervised Machine Learning
This topic will include classical and current algorithms for unsupervised learning from machine learning and statistics including hierarchical and parititional algorithms (K-means,Fuzzy C-means, Gaussian EM, graph partitioning, density based algorithms, grid based algorithms, unsupervised ANN, affinity propagation, ...)
Unsupervised methodologies in Knowledge Discovery and Data Mining
This topic will include current trends on knowledge discovery for data mining and big data, (scalability, any time clustering, one pass algorithms, approximation algorithms, distributed clustering, ..)
Advanced topics in unsupervised learning
This topic will include and introduction to different advanced topics in unsupervised learning such as consensus clustering, subspace clustering, biclustering and semisupervised clustering
Unsupervised learning for sequential and structured data
This topic will include algorithms for unsupervised learning with sequential data and structured data, such as sequences, strings, time series and data streams, graphs and social networks
Unsupervised Deep Learning: Autoregressive and Flow models
We will see algorithms able to estimate probability distribution models from unsupervised data that can be sampled to generate new data assuming autoregressive dependencies and flow transference models
Unsupervised Deep Learning: Latent Variable models, Autoencoders and Variational Autoencoders
This topic will introduce to latent variable models for learning of probabilistic models of data and latent representations for sampling and generating data for applications in image and text generation
Unsupervised Deep Learning: Implicit models, Generative Adversarial Networks
This topic will introduce to models that represent implicitly probability distribution models using adversarial learning. Different models based on Generative Adversarial Networks will be explained following its evolution since their original formulation. Different applications to image generation will be explained.
Unsupervised Deep Learning: Self-supervised and Contrastive learning
This topic will introduce to models for learning representations to be used for other tasks using self-supervised methodologies and contrastive learning. Different approaches for defining the unsupervised task used to learn a representation will be explained in the context of applications for image and text.
This activity develops the topics of the unsupervised learning part of the course
The evaluation will be based on an individual questionnaire about the topics of the course (30%) and a coursework to choose between to write a report on the state of the art for a particular topic of the course or to implement machine learning algorithms (70%).