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

From: coss <cossorzano_at_gmail.com>
Date: Mon, 7 Apr 2008 08:55:46 -0700 (PDT)
Message-ID: <1a65a9f8-fa4a-471c-965f-ce13c3aa85f3_at_k1g2000prb.googlegroups.com>

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

Dear colleagues,

San Pablo - CEU University in collaboration with other five universities (Málaga,
Politécnica de Madrid, País Vasco, Complutense, and Castilla La Mancha), Unión 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

Received on Mon Apr 07 2008 - 17:55:46 CEST