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Notice

AISB event Bulletin Item

Second CEU Summerschool on Advanced Data Analysis and Modelling

http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm.

Dear colleagues,

San Pablo - CEU University in collaboration with other five universities (Mlaga,
Politcnica de Madrid, Pas Vasco, Complutense, and Castilla La Mancha), SPSS, CSIC and IEEE
organizes a summerschool on "Advanced Data Analysis and Modeling" in Madrid between July
9th and July 27th. The summerschool comprises 12 courses divided in 3 modules.
Attendees may register in each course independently. Registration will be considered upon
strict arrival order.For more information, please, visit
http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm.

Best regards, Carlos Oscar

*List of courses and brief description* (full description at
http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/ADAM.htm)

COURSE 1. REGRESSION (July 9th-July 13th)
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, Biased estimations of
regression coefficients to deal with multicollinearity, Nonlinear Regression, Robust
Regression, Nonparametric Regression. Practical demonstration: SPSS

COURSE 2. ASSOCIATION RULES (July 9th-July 13th)
Introduction, Association rule discovering, Rule induction, KDD in biological data,
Applications, Hands-on exercises. Practical demonstration: Bioinformatic tools

COURSE 3. STATISTICAL INFERENCE (July 9th-July 13th)
Introduction, Some basic statistical test, Multiple testing. Practical demonstration: SPSS

COURSE 4. DIMENSIONALITY REDUCTION (July 9th-July 13th)
Introduction, Matrix factorization methods, Projection methods, Applications,
Practical excercises. Practical Demonstration: MATLAB and Web applications

COURSE 5. BAYESIAN NETWORKS (July 16th-July 20th)
Bayesian networks basics, Inference in Bayesian networks, Learning Bayesian networks
from data. Practical demonstration: Hugin, Elvira, Weka, LibB.

COURSE 6. HIDDEN MARKOV MODELS (July 16th-July 20th)
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.
Practical demonstration: The HTK toolkit

COURSE 7. NEURAL NETWORKS (July 16th-July 20th)
Introduction to the biological models, Perceptron networks, The Hebb rule, Foundations
of multivariate optimization, Numerical optimization, Rule of Widrow-Hoff, Backpropagation
algorithm, Practical data modelling with neural networks. Practical demonstration:
MATLAB Neural network toolbox

COURSE 8. TIME SERIES ANALYSIS (July 16th-July 20th)
Introduction, Probability models to time series, Regression and Fourier analysis,
Forecasting and Data mining. Practical demonstration: MATLAB

COURSE 9. MULTIVARIATE DATA ANALYSIS (July 23rd-July 27th)
Introduction, Data Examination, Principal component analysis (PCA), Factor Analysis,
Multidimensional Scaling (MDS), Correspondence analysis, Multivariate Analysis of
Variance (MANOVA). Practical demonstration: MATLAB

COURSE 10. SUPERVISED PATTERN RECOGNITION (July 23rd-July 27th)
Introduction, Assessing the Performance of Supervised Classification Algorithms,
Classification techniques, Combining Classifiers, Comparing Supervised Classification
Algorithms. Practical demonstration: WEKA

COURSE 11. EXPERT SYSTEMS (July 23rd-July 27th)
Introduction to Expert Systems and Knowledge Based Systems, Expert System Programming,
Hybrid Systems, Imprecision and uncertainty. Practical demonstration: CLIPS and JESS

COURSE 12. CLUSTERING (July 23rd-July 27th)
Introduction, Exploring Data, Preprocessing, Distance metric, Clustering Techniques,
Anomaly Detection. Practical demonstration: MATLAB