Third CEU Summerschool on Advanced Statistics and Data Mining (June 30th-July 11th, 2008)

Posted by coss on April 7, 2008, 11:57 am

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), Uni=F3n Fenosa, CSIC and IEEE
organizes a summerschool on "Advanced Statistics and Data Mining" in
Madrid between June 30th
and July 11th. The summerschool comprises 12 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
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)

Week 1 (June 30th - July 4th, 2008)

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: 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 4: Association rules (15 h), Practical sessions: Bioinformatic
tools
        Introduction. Association rule discovering. Rule Induction. KDD in
biological data.
        Applications. Hands-on exercises.
Course 5: 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 6: Time series analysis (15 h), Practical sessions: MATLAB
        Introduction. Probability models to time series. Regression and
Fourier analysis.
        Forecasting and Data mining.

Week 2 (July 7th - July 11th, 2008)
Course 7: 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 8: 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 9: 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 10: Statistical inference (15 h), Practical sessions: SPSS
        Introduction. Some basic statistical test. Multiple testing.
Introduction to bootstrapping
Course 11: Dimensionality reduction (15 h), Practical sessions: MATLAB
        Introduction. Matrix factorization methods. Clustering methods.
Projection methods.
        Applications
Course 12: Unsupervised pattern recognition (clustering) (15 h),
Practical sessions: MATLAB
        Introduction. Prototype-based clustering. Density-based clustering.
        Graph-based clustering. Cluster evaluation. Miscellanea

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