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Fifth Summer School on Advanced Statistics and Data Mining in Madrid

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  • namnof
    Dear colleagues, the Technical University of Madrid (UPM) again organizes the summer school on Advanced Statistics and Data Mining in Madrid between June
    Message 1 of 1 , May 24, 2010
      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.


      Best regards.

      P. LarraƱaga and R. ArmaƱanzas.

      *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: 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 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: 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 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.

      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.
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