Machine Learning Group

Calendario de sesiones

Temporada 2017/2018

Fecha Aula Hora Ponente Título Material
//2018 TBA 9:40 ... ... ...
23/05/2018 TBA 9:40 ... ... ...
16/05/2018 TBA 9:40 Carlos Sevilla ... ...
09/05/2018 TBA 9:40 Jesús Cid [DL] 20. Deep Generative Models ...
25/04/2018 TBA 9:40 Pablo Martínez ... ...
18/04/2018 TBA 9:40 Manuel Vázquez [DL] 19. Approximate Inference ...
11/04/2018 TBA 9:40 Luis Azpicueta ... ...
04/04/2018 TBA 9:40 Jerónimo Arenas [DL] 18. Confronting the Partition Function... ...
28/03/2018 TBA 9:40 Ángel Navia ... ...
21/03/2018 TBA 9:40 Vanessa Gómez [DL] 17. Monte Carlo Methods ...
14/03/2018 TBA 9:40 Simón Roca ... ...
07/03/2018 TBA 9:40 Lorena Álvarez [DL] 16. Structured Probabilistic Models for Deep Learning ...
28/02/2018 TBA 9:40 Adil Omari ... ...
21/02/2018 TBA 9:40 Diego Rojo [DL] 15. Representation Learning ...
14/02/2018 TBA 9:40 Óscar García ... ...
07/02/2018 TBA 9:40 Carlos Sevilla [DL] 14. Autoencoders ...
31/01/2018 TBA 9:40 Jesús Cid ... ...
24/01/2018 TBA 9:40 Pablo Martínez [DL] 13. Linear Factor Models ...
17/01/2018 TBA 9:40 Luis Azpicueta ... ...
10/01/2018 TBA 9:40 Manuel Vázquez [DL] 11. Practical Methodology and 12. Applications ...
20/12/2017 4.2.E02 9:40 Jerónimo Arenas ... ...
13/12/2017 4.2.E02 9:40 Ángel Navia [DL] 10. Sequence Modelling: Recurrent and Recursive Nets ...
29/11/2017 4.2.E02 9:40 Vanessa Gómez Bayesian Canonical Correlation Analysis ...
22/11/2017 4.2.E02 9:40 Simón Roca [DL] 9. Convolutional Networks ...
15/11/2017 4.2.E02 9:40 Lorena Álvarez Semi-supervised learning Slides
08/11/2017 4.2.E02 9:40 Adil Omari [DL] 7. Regularization for Deep Learning and 8. Optimization for Training Deep Models ...
31/10/2017 4.0.E05 9:40 Diego Rojo Text Categorization from Category Names ...
18/10/2017 4.2.E02 9:40 Óscar García [DL] 6. Deep Feedforward Networks ...
20/09/2017 4.2.E02 9:40 ... Preparatory session: content of the next talks. ...

Temporada 2016/2017

Fecha Aula Hora Ponente Título Material
19/07/2017 7.1.J03 9:30 Jesús Cid-Sueiro PSBI (4): Scaling Variational Mean Field Algorithms ...
12/07/2017 4.2.E02 9:30 Abdel Moujahid The Metabolic Cost of Neuronal Activity ...
04/07/2017 4.2.E02 9:30 Pablo Martínez-Olmos PSBI (3): Parallel and distributed MCMC ...
29/06/2017 4.2.E02 9:30 Manuel Vázquez-López PSBI (2): MCMC with data subsets Slides
20/06/2017 4.2.E02 9:30 Luis Azpicueta Machine Learning Methods to Predict Diabetes Complications ...
14/06/2017 4.2.E02 9:30 Jerónimo Arenas-García Patterns of Scalable Bayesian Inference (1): Background Slides, Notes
07/06/2017 7.1.J02 9:30 Ángel Navia-Vázquez Natural Language Processing ...
31/05/2017 4.2.E02 9:30 Vanessa Gómez-Verdejo Building my Internet Search Engine: Scrapy and Lucene Slides and code
17/05/2017 4.2.E02 9:30 Simón Roca-Sotelo Topic Models and Word Embeddings Slides
10/05/2017 4.2.E02 9:30 Lorena Álvarez Pérez Training neural networks classifiers through Bayes risk minimization using Parzen windows ...
03/05/2017 4.2.E02 9:30 Adil Omari Deep Neural Networks for Wind and Solar Energy Prediction pdf
26/04/2017 4.2.E02 9:30 Diego Rojo García Visualización en Python con Bokeh ...
19/04/2017 4.2.E02 9:30 Jesús Cid-Sueiro Convex Optimization (Cap 11: Interior Point Methods) ...
05/04/2017 4.2.E02 9:30 Óscar García-Hinde Neo4j Slides, Tutorial
29/03/2017 4.2.E02 9:30 Henry Navarro Convex Optimization (Cap 10: Equality Constrained Minimization) ...
22/03/2017 4.2.E02 9:30 Jerónimo Arenas-García SQL ppt, csv ...
15/03/2017 4.2.E02 9:30 Ángel Navia-Vázquez Convex Optimization (Cap 9: Unconstrained Minimization) ...
01/03/2017 4.2.E02 9:30 Jesús Cid-Sueiro Convex Optimization (Cap 8: Geometric Problems) ...
22/02/2017 4.2.E02 9:30 Vanessa Gómez-Verdejo Spark with Dataframes ...
15/02/2017 4.2.E02 9:30 Luis Azpicueta Google Fusion Tables ...
08/02/2017 4.2.E02 9:30 Simón Roca-Sotelo Convex Optimization (Cap 7: Statistical Estimation) ...
01/02/2017 4.2.E02 9:30 Lorena Álvarez-Pérez Qlik ...
25/01/2017 4.2.E02 9:30 Abdel Moujahid (1) A Nonlinear Dynamic Approach to Mouse Behavior Classification, and (2) Object Detection based on Community Identification in Graphs slides
18/01/2017 4.0.E04 9:30 Oscar García-Hinde Convex Optimization (Cap 6: Approximation and Fitting) ...
11/01/2017 4.0.E04 9:30 Henry Navarro Tableau SW
21/12/2016 4.2.E02 9:45 Fernando García Machine Learning en el Tratamiento de la Diabetes Tipo 1 ...
14/12/2016 4.0.D01 12:00 Miguel Lázaro Hierarchical Compositional Feature Learning ...
12/12/2016 4.0.D01 12:00 Darío García ML & AI @ FB ...
23/11/2016 4.2.E02 9:30 Jerónimo Arenas-García Convex Optimization (Cap 5: Duality (II)) ...
16/11/2016 4.2.E03 9:30 Manuel Vázquez-López Convex Optimization (Cap 5: Duality (I)) ...
09/11/2016 4.2.E02 9:30 Ángel Navia-Vázquez Carto ...
02/11/2016 4.2.E02 9:30 Sergio Muñoz-Romero Convex Optimization (Cap 4: Convex Optimization Problems) ...
26/10/2016 4.2.E02 9:30 Jesús Cid-Sueiro Gephi Tutorial
19/10/2016 4.2.E02 9:30 Luis Azpicueta Convex Optimization (Cap. 3: Convex Functions) ...
07/10/2016 4.2.E03 9:30 Vanessa Gómez Verdejo Convex Optimization (Cap. 2: Convex Sets) ...

Temporada 2015/2016

Fecha Aula Hora Ponente Título Material
21/06/2016 4.1.E02 11:00 Juan José Choquehuanca Generative Adversarial Nets pdf
14/06/2016 4.1.E02 11:00 Óscar García-Hinde Consistent Algorithms for Clustering Time Series pdf
07/06/2016 7.2.H01 9:30 Henry Navarro Cross Collection Topic Models pdf
31/05/2016 4.2.E02 9:30 Raúl Moreno-Salinas An Introduction to Symbolic Regression ...
17/05/2016 4.2.E03 9:40 Jerónimo Arenas-García Gaussian Processes for Big Data pdf
10/05/2016 4.2.E03 9:30 Ángel Navia-Vázquez Overview of Apache Flink: Next-Gen Big Data Analytics Framework ...
03/05/2016 4.2.E03 9:30 Sergio Muñoz-Romero Towards a neocortex-inspired machine learning pdf, html
19/04/2016 4.2.E03 9:30 Jesús Cid-Sueiro A survey on Sentiment Analysis pdf
12/04/2016 4.2.E03 9:30 Luis Azpicueta Noise pollution predition: noise maps + machine learning pdf
05/04/2016 4.2.E03 9:30 Vanessa Gómez-Verdejo A Survey on Transfer Learning pdf
08/03/2016 4.2.E03 9:30 Juan José Choquehuanca Spectral Representations for Convolutional Neural Nets NIPS2015
01/03/2016 4.2.E03 9:30 Oscar García-Hinde Feature Selection in Solar Radiation Prediction Using Bootstrapped SVRs pdf
23/02/2016 4.2.E03 9:30 Henry Navarro The Tie Decay Problem in Social Networks: Why Do People Stop Calling? ...
16/02/2016 4.2.E03 9:30 Raúl Moreno-Salinas Disruption Prediction in Nuclear Fusion Devices Art1 , Art2 , Art3 , Thesis
09/02/2016 4.2.E03 9:30 Jesús Fernández-Bes Introduction to Convolutional Networks using TensorFlow web
01/02/2016 4.0.E02 12:00 Jerónimo Arenas-García BDAS (Lab4): Machine Learning with Apache Spark ...
20/01/2016 4.2.E03 9:30 Ángel Navia-Vázquez BDAS (L7+L8+Lab3): Data Quality, Exploratory Analysis and Machine Learning (y 2) ...
13/01/2015 4.0.E02 9:30 Ángel Navia-Vázquez BDAS (L7+L8+Lab3): Data Quality, Exploratory Analysis and Machine Learning ...
16/12/2015 4.0.E02 9:30 Sergio Muñoz-Romero BDAS (L5+L6+Lab2): Data Management PySpark pictures
09/12/2015 4.0.E02 9:30 Jesús Cid-Sueiro BDAS (Lab1): Introduction to Apache Spark ...
02/12/2015 4.0.E02 9:30 Luis Azpicueta BDAS (L3+L4): Introduction to Apache Spark ...
25/11/2015 4.0.E02 9:30 Rocío Arroyo-Valles Big Data with Apache Spark (L1+L2): Introduction and SW setup Slides
18/11/2015 4.0.E02 9:30 Vanessa Gómez Verdejo Hadoop and Map Reduce for Dummies ...

Temporada 2014/2015

Fecha Aula Hora Ponente Título Material
06/07/2015 4.2.E03 11:00 Oscar García Hinde A.Nguyen, J. Yosinski, J. Clune, "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images" pdf
22/06/2015 4.2.E03 11:00 Jerónimo Arenas-García Collapsed Gibbs Sampling for Latent Dirichlet Allocation on Spark pdf
19/06/2015 4.0.E04 12:00 Miguel Lázaro Gredilla Towards Human-Level AI Vicarious
10/06/2015 7.1.H01 10:00 Jesús Fernández-Bes Algorithms for Energy-Efficient Adaptive Wireless Sensor Networks Tesis
01/06/2015 4.2.E03 12:00 Mrityunjoy Chakraborty Sparse Distributed Estimation via Heterogeneous Diffusion Adaptive Networks ...
25/05/2015 4.2.E03 11:00 Ángel Navia-Vázquez Procesado en array: núcleos y aproximación geométrica doi1, doi2, tutorial1, tutorial2,
11/05/2015 4.2.E03 11:00 Sergio Muñoz-Romero Análisis multivariante: soluciones eficientes e interpretables Tesis
27/04/2015 4.2.E03 11:00 Jesús Cid-Sueiro The U/P assignment problem for lifting transforms on graphs ...
23/04/2015 4.2.E02 10:30 Vanessa Gómez-Verdejo * Model Predictive Regulation pdf
13/04/2015 4.2.E03 11:00 Luis Azpicueta * Battery Storage System sizing in distribution feeders with distributed photovoltaic systems doi
23/03/2015 4.0.E05 11:00 Sergio Muñoz-Romero * Economic viability of energy storage systems based on price arbitrage potential in real-time U.S. electricity markets doi
16/03/2015 4.0.E05 11:00 Jesús Fernández-Bes * Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application doi
11/03/2015 2.1.C08 11:00 Rocío Arroyo-Valles * Stochastic Optimal Control of the Storage System to Limit Ramp Rates of Wind Power Output doi
03/03/2015 4.0.E04 14:00 Oscar García Hinde * Analysis of battery storage utilization for load shifting and peak smoothing on a distribution feeder in New Mexico doi

* Las sesiones del Smart-Grid track se han organizado de acuerdo con el documento de referencia elaborado por Manel y Vanessa, que podéis encontrar aquí

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