Monday, May 20, 2013

[Comp-neuro] Summer School on Advanced Statistics and Data Mining (Madrid) - Early registration deadline

Dear colleagues,

We would like to remind you that the early registration period for the
school on 'Advanced Statistics and Data Mining' will finish on the 1st
of june.
The summer school will be organized by the Technical University of Madrid
(UPM) between June 24th and July 5th. This year's programme comprises 12
courses divided into 2 weeks. Attendees may register in each course

Early registration is now OPEN. Extended information on course programmes,
price, venue, accommodation and transport is available at the school's

Please, send this information to your colleagues, students, and whoever may
find it interesting.

Best regards,

Pedro Larrañaga, Concha Bielza and Pedro L. López-Cruz.
-- The coordinators of the school.

*** List of courses and brief description ***

* Week 1 (June 24th - June 28th, 2013) *

1st session: 9:30 - 12:30
Course 1: Bayesian networks (15 h)
Basics of Bayesian networks. Inference in Bayesian networks.
Learning Bayesian networks from data. Real applications.

Course 2: Statistical inference (15 h)
Introduction. Some basic statistical test. Multiple testing.
Introduction to bootstrap methods. Introduction to Robust Statistics.

2nd session: 13:30 - 16:30
Course 3: Supervised pattern recognition (15 h)
Introduction. Assessing the performance of supervised classification
algorithms. Preprocessing. Classification techniques. Combining
multiple classifiers. Comparing supervised classification algorithms.

Course 4: Multivariate data analysis (15 h)
Introduction. Data examination. Principal component analysis.
Factor Analysis. Multidimensional scaling. Correspondence
analysis. Tensor analysis. Multivariate Analysis of Variance.
Canonical Correlation Analysis. Latent Class Analysis.

3rd session: 17:00 - 20:00
Course 5: Neural networks (15 h)
Introduction. Perceptrons. Training algorithms. Accelerating
convergence. Useful tricks for MLPs. Deep networks. Practical
data modelling with neural networks.

Course 6: Feature Subset Selection (15 h)
Introduction. Filter approaches. Wrapper methods.
Embedded methods. Drawbacks and future strands.
Practical session.

* Week 2 (July 1st - July 5th, 2013) *

1st session: 9:30 - 12:30
Course 7: Time series analysis (15 h)
Introduction. Probability models to time series. Regression and
Fourier analysis. Forecasting and Data mining.

Course 8: Hidden Markov Models (15 h)
Introduction. Discrete Hidden Markov Models. Basic algorithms
for Hidden Markov Models. Semicontinuous Hidden Markov Models.
Continuous Hidden Markov Models. Unit selection and clustering.
Speaker and Environment Adaptation for HMMs.
Other applications of HMMs.

2nd session: 13:30 - 16:30
Course 9: Bayesian classifiers (15 h)
Discrete predictors. Gaussian Bayesian networks-based classifiers.
Other Bayesian classifiers. Bayesian classifiers for: positive and
unlabeled data, semi-supervised learning, data streams, temporal

Course 10: Unsupervised pattern recognition (15 h)
Introduction. Prototype-based clustering. Density-based
clustering. Graph-based clustering. Cluster evaluation.

3rd session: 17:00 - 20:00
Course 11: Support vector machines, regularization and convex
optimization (15 h)
Introduction. SVM models. SVM learning algorithms. Convex
non differentiable optimization.

Course 12: Hot topics in intelligent data analysis (15 h)
Multi-label and multi-dimensional classification. Multi-dimensional
classification and multi-output regression. Advanced Clustering.
Partially supervised classification with uncertain class labels.
Directional statistics. Spatial point processes.

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