Dear colleagues, The Technical University of Madrid (UPM) again organizes the summer school on 'Advanced Statistics and Data Mining' in Madrid between July 4th and July 15th. This year's programme comprises 17 courses divided in 2 weeks. There are new 5 courses not previously offered in the previous edition. Attendees may register in each course independently. Registration is now *OPEN*. Extended information on courses programme, 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 (July 4th - July 8th, 2011) Course 1: Bayesian networks (15 h) Bayesian networks basics. Inference in Bayesian networks. Learning Bayesian networks from data. Course 2: Statistical inference (15 h) Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrapping. Course 3: Probabilistic modelling for evolutionary computation (15 h) Evolutionary algorithms. Estimation of distribution algorithms. EDAs for discrete, continuous and multi objective optimization problems. Real-world applications. Course 4: Supervised pattern recognition (Classification) (15 h) Introduction. Assessing the Performance of Supervised Classification Algorithms. Classification techniques. Combining Classifiers. Comparing Supervised Classification Algorithms. Course 5: 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 6: Neural networks (15 h) Introduction to the biological models. Nomenclature. Perceptron networks. The Hebb rule. Foundations of multivariate optimization. Numerical optimization. Rule of Widrow-Hoff. Backpropagation algorithm. Practical data modelling with neural networks. Course 7: Features Subset Selection (15 h) Introduction. Redundancy and irrelevance. Filter approaches. Wrapper methods. Embedded methods. Drawbacks and future strands. Stability and consistency. Practical session with presentation. Course 8: 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. Non-linear Regression. Week 2 (July 11th - July 15th, 2011) Course 9: Hot topics in intelligent data analysis (15 h) Multi-label and multi-dimensional classification. Advanced Bayesian classifiers. Data streams in a semi-supervised learning context. Advanced Clustering. Spatial and circular point patterns. Course 10: Machine learning in computer vision (15 h) The scene understanding problem. Visual features for object detection and classification. Usual classification techniques in computer vision. Object detection. Object classification. Course 11: Hidden Markov Models (15 h) Introduction. Discrete Hidden Markov Models. Basic algorithms for Hidden Markov Models. Semi-continuous Hidden Markov Models. Continuous Hidden Markov Models. Unit selection and clustering. Speaker and Environment Adaptation for HMMs. Other applications of HMMs. Course 12: Time series analysis (15 h) Introduction. Probability models to time series. Regression and Fourier analysis. Forecasting and Data mining. Course 13: Data mining: A practical perspective (15 h) Introduction to Data Mining and Knowledge Discovery. Prediction in data mining. Classification. Association studies. Data mining in free-form texts: text mining. Course 14: Unsupervised pattern recognition (clustering) (15 h) Introduction. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Course 15: Support vector machines and kernel methods (15 h) Linear classifiers. Perceptrons. Linear SMVs. Non-linear SVMs. Kernelization. Support Vector Regression. Related models. SVM Learning algorithms. Kernel PCA. Kernel FDA. Kernel K-means. Course 16: Practical statistical questions (15 h) The basics. How do I collect the data? Experimental design. Parameter estimation. Correlation. Hypothesis testing. Sample size. Study of cases of different fields. Course 17: Statistics and machine learning with R (15h) The R environment. Data in R. Programming in R. Graphics in R. Statistical Analysis with R. Practical sessions