Dear colleagues, the Technical University of Madrid (UPM) again organizes the summer school on 'Advanced Statistics and Data Mining' in Madrid between June 28th and July 9th. This year programme comprises 14 courses divided in 2 weeks. Attendees may register in each course independently.
Information on the registration process will be available soon in the web page. Please, visit the following site for more information. http://www.dia.fi.upm.es/ASDM2010 Best regards. P. Larrañaga and R. Armañanzas. *List of courses and brief description* Week 1 (June 28th - July 2nd, 2010) Course 1: Bayesian networks (15 h) Bayesian networks basics. Inference in Bayesian networks. Learning Bayesian networks from data. Course 2: 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. Nonlinear Regression. Course 3: 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 4: 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 5: Dimensionality reduction (15 h) Introduction. Matrix factorization methods. Clustering methods. Projection methods. Applications. Course 6: Supervised pattern recognition (Classification) (15 h) Introduction. Assessing the Performance of Supervised Classification Algorithms. Classification techniques. Combining Classifiers. Comparing Supervised Classification Algorithms. Course 7: Evolutionary computation (15 h) Genetic algorithms. Genetic programming. Robust and self-adapting intelligent systems. Introduction to Estimation of Distribution Algorithms. Improvements, extensions and applications of EDAs. Current research in EDAs. Week 2 (July 5th - July 9th, 2010) Course 8: Datamining: 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 9: 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 10: Time series analysis (15 h) Introduction. Probability models to time series. Regression and Fourier analysis. Forecasting and Data mining. Course 11: Features Subset Selection (15 h) Introduction. Redundance and irrelevance. Filter approaches. Wrapper methods. Embedded methods. Course 12: Statistical inference (15 h) Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrapping. Course 13: Unsupervised pattern recognition (clustering) (15 h) Introduction. Prototype-based clustering. Density-based clustering. Graph-based clustering. Cluster evaluation. Miscellanea. Course 14: Introduction to R (15 h) An introductory R session. Data in R. Importing/Exporting data. Programming in R. R Graphics. Statistical Functions in R. _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
