[Comp-neuro] Eighth Summer School on Advanced Statistics and Data Mining (Madrid, June 24th - July 5th, 2013)

Pedro Luis López Cruz pedro.lcruz at upm.es
Mon Mar 18 16:54:04 CET 2013


Dear colleagues,

The Technical University of Madrid (UPM) will once more organize the summer
school on 'Advanced Statistics and Data Mining' in Madrid between June
24th and July 5th. This year's programme comprises 12 courses divided
into 2 weeks. Attendees may register in each course independently.

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

http://www.dia.fi.upm.es/ASDM

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
       data.

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

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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