[Comp-neuro] 7th Summer School on Advanced Statistics and Data Mining (Madrid, June 25th - July 6th, 2012)

(Apologies for multiple posting)

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
25th and July 6th. 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

Best regards,

-- The coordinators of the school.

*List of courses and brief description*

Week 1 (June 25th - June 29th, 2012)

Course 1: Bayesian networks (15 h)
Basics of Bayesian networks. Inference in Bayesian networks.
Learning Bayesian networks from data.

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

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
(PCA). Factor Analysis. Multidimensional Scaling (MDS).
Correspondence analysis. Multivariate Analysis of Variance
(MANOVA). Canonical correlation.

Course 5: Neural networks (15 h)
Introduction. Perceptrons. Training algorithms. Accelerating
convergence. Useful tricks for MLPs. Deep networks.

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

Week 2 (July 2nd - July 6th, 2012)

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.

Course 9: Hot topics in intelligent data analysis (15 h)
Multi-label and multi-dimensional classification. Advanced
Bayesian classifiers. Advanced Clustering. Partially supervised
classification with uncertain class labels. Directional statistics.
Spatial point processes.

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

Course 11: Support vector machines and kernel methods (15 h)
Introduction. SVM models. SVM learning algorithms. Other kernel
methods: Kernel PCA, Kernel FDA, Kernel K-means. Practical
work.

Course 12: Regression (15 h)
Introduction. Simple Linear Regression Model. Measures of model
adequacy. Multiple Linear Regression. Regression Diagnostics and
model violations. Polynomial regression. Variable selection.
Indicator variables as regressors. Logistic regression.

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