Unsupervised and Reinforcement Learning

Teachers

Person in charge

• Javier Béjar Alonso ( bejar@cs.upc.edu )

Weekly hours

Objectives

1. To known and use advanced unsupervised machine learning techniques for application on all the domains of engineering and science
   Related competences: CB7, CT2, CT4, CEP1, CG3, CEA12, CEA13, CG1,

Contents

1. 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
2. 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)
3. 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, ...)
4. 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, ..)
5. 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
6. 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
7. 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
8. 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
9. 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.
10. Unsupervised Deep Learning: Diffusion Models
    This topic will introduce to generative models bases on latent variables that match a noise gaussian distribution with the data distribution using a discrete or continuous process in time simulating the physics of diffusion. We will see the formulation of these models as a stochastic or a deterministic process and its connection to differential equations
11. 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.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic

• Data clustering : algorithms and applications - Aggarwal, C. C., & Reddy, C. K, CRC Press, 2014. ISBN: 9781466558212
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004187309706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Data mining and machine learning: fundamental concepts and algorithms - Zaki, M.J.; Meira, W, Cambridge University Press, 2020. ISBN: 9781108473989
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004209799706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Deep learning - Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y, MIT press, [2016]. ISBN: 9780262035613
  https://www.deeplearningbook.org/

Web links

• Material for the course https://sites.google.com/upc.edu/mai-url

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