Compression - Second CEU Summerschool on Advanced Data Analysis and Modelling (July 9th-27th, 2007)

Author

Second CEU Summerschool on Advanced Data Analysis and Modelling (July 9th-27th, 2007)

coss

2007-03-17, 6:56 pm

Dear colleagues,

San Pablo - CEU University in collaboration with other five
universities (M=E1laga,
Polit=E9cnica de Madrid, Pa=EDs 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/Do.../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: SPSS

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

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