This is a discussion on Third CEU Summerschool on Advanced Statistics and Data Mining (June30th-July 11th, 2008) - Theory ; 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 ...
San Pablo - CEU University in collaboration with other five
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
Attendees may register in each course independently. Registration will
be considered upon
strict arrival order.For more information, please, visit
Best regards, Carlos Oscar
*List of courses and brief description* (full description at
Week 1 (June 30th - July 4th, 2008)
Course 1: Bayesian networks (15 h), Practical sessions: Hugin, Elvira,
Bayesian networks basics. Inference in Bayesian networks.
Learning Bayesian networks from data
Course 2: Multivariate data ****ysis (15 h), Practical sessions:
Introduction. Data Examination. Principal component ****ysis (PCA).
Factor ****ysis. Multidimensional Scaling (MDS). Correspondence
Multivariate ****ysis of Variance (MANOVA). Canonical correlation.
Course 3: Supervised pattern recognition (Classification) (15 h),
Practical sessions: Weka
Introduction. Assessing the Performance of Supervised Classification
Classification techniques. Combining Classifiers.
Comparing Supervised Classification Algorithms
Course 4: Association rules (15 h), Practical sessions: Bioinformatic
Introduction. Association rule discovering. Rule Induction. KDD in
Applications. Hands-on exercises.
Course 5: Neural networks (15 h), Practical sessions: MATLAB
Introduction to the biological models. Nomenclature. Perceptron
The Hebb rule. Foundations of multivariate optimization. Numerical
Rule of Widrow-Hoff. Backpropagation algorithm.
Practical data modelling with neural networks
Course 6: Time series ****ysis (15 h), Practical sessions: MATLAB
Introduction. Probability models to time series. Regression and
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
Multiple Linear Regression. Regression Diagnostics and model
Polynomial regression. Variable selection. Indicator variables as
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
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
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
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.
Course 12: Unsupervised pattern recognition (clustering) (15 h),
Practical sessions: MATLAB
Introduction. Prototype-based clustering. Density-based clustering.
Graph-based clustering. Cluster evaluation. Miscellanea