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Notice

AISB opportunities Bulletin Item

Summer School on Advanced Statistics and Data Mining, Madrid, SPAIN

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

Dear colleagues,

We would like to remind you that the early registration period for the 
summer 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 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 Larraaga, Concha Bielza and Pedro L. Lpez-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.