Fourth Summerschool on Advanced Statistics and Data Mining
The Polytechnical Univ. of Madrid organizes a summerschool on “Advanced Statistics and Data Mining” in Madrid between July 6th and July 17th. The summerschool comprises 18 courses divided in 2 weeks.
Attendees may register in each course independently. Registration will be considered upon strict arrival order.
For more information, please, visit
List of courses and brief description
(Full description at http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm)
Week 1 (July 6th – July 10th, 2009)
Course 1: Bayesian networks (15 h), Practical sessions: Hugin, Elvira, Weka, LibB
Bayesian networks basics. Inference in Bayesian networks.
Learning Bayesian networks from data
Course 2: Multivariate data analysis (15 h), Practical sessions: MATLAB
Introduction. Data Examination. Principal component analysis (PCA).
Factor Analysis. Multidimensional Scaling (MDS). Correspondence analysis.
Multivariate Analysis of Variance (MANOVA). Canonical correlation.
Course 3: Dimensionality reduction (15 h), Practical sessions: MATLAB
Introduction. Matrix factorization methods. Clustering methods. Projection methods. Applications
Course 4: Supervised pattern recognition (Classification) (15 h), Practical sessions: Weka
Introduction. Assessing the Performance of Supervised Classification Algorithms.
Classification techniques. Combining Classifiers.
Comparing Supervised Classification Algorithms
Course 5: Introduction to MATLAB (15 h)
Overview of the Matlab suite. Data structures and files. Programming in Matlab.
Visualization tools. Some applications in pattern recognition.
Course 6: Datamining, a practical perspective (15h), Practical sessions: MATLAB, R, Weka
Introduction to Data Mining and Knowledge Discovery. Prediction in data mining.
Classification. Association studies. Data mining in free-form texts: text mining.
Course 7: Time series analysis (15 h), Practical sessions: MATLAB
Introduction. Probability models to time series. Regression and Fourier analysis.
Forecasting and Data mining.
Course 8: Neural networks (15 h), Practical sessions: MATLAB
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 9: Introduction to SPSS (15 h)
Introduction. Describing data. Statistical inference. Time series. Sampling.
Classification and regression
Week 2 (July 13th – July 17th, 2009)
Course 10: Regression (15 h), Practical sessions: SPSS
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 11: Practical Statistical Questions (15 h), Practical sessions: study of cases (without computer)
I would like to know the intuitive definition and use of …: The basics.
How do I collect the data? Experimental design.
Now I have data, how do I extract information? Parameter estimation
Can I see any interesting association between two variables, two populations, …?
How can I know if what I see is “true”? Hypothesis testing
How many samples do I need for my test?: Sample size
Can I deduce a model for my data? Other questions?
Course 12: Missing data and outliers (15 h), Practical sessions: R
Missing Data: Typology of missing data; Simple missing-data methods;
Imputation Methods; Diagnostics and Overimputing. Outliers and robust statistics:
Typology of outliers; Influence measures; Robust methods
Course 13: Hidden Markov Models (15 h), Practical sessions:HTK
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 14: Statistical inference (15 h), Practical sessions: SPSS
Introduction. Some basic statistical test. Multiple testing. Introduction to bootstrapping
Course 15: Features Subset Selection (15 h), Practical sessions: MATLAB, R, Weka
Filter approaches. Wrapper methods. Embedded methods.
Course 16: Introduction to R (15 h)
An introductory R session. Data in R. Importing/Exporting data. Programming in R.
R Graphics. Statistical Functions in R
Course 17: Unsupervised pattern recognition (clustering) (15 h), Practical sessions: MATLAB
Introduction. Prototype-based clustering. Density-based clustering.
Graph-based clustering. Cluster evaluation. Miscellanea
Course 18: Evolutionary computation (15 h), Practical sessions: MATLAB
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.