[Computational-biology] Fourth Summerschool on Advanced Statistics
and Data Mining (Madrid, July 6th-17th, 2009)
coss.eps from ceu.es
(by coss.eps from ceu.es)
Fri May 15 04:10:26 EST 2009
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
Attendees may register in each course independently. Registration will
considered upon strict arrival order.For more information, please,
Best regards, Carlos Oscar
*List of courses and brief description* (full description at
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:
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
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.
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
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.
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.
More information about the Comp-bio