Dear colleagues,
We would like to remind you that the early registration period for the Advanced
Statistics and Data Mining Summer School will finish on June 2nd. The summer
school will be held in Madrid, from June 23rd to July 4th. This year's
programme comprises 12 courses and runs for 2 weeks. Attendees may register in
each course independently. Extended information on course programmes, price,
venue, accommodation and transport is available at the school's website:
http://www.dia.fi.upm.es/ASDM
Please, send this information to your colleagues, students, and whoever you
think may find it interesting.
Best regards,
Pedro Larranaga, Concha Bielza, Bojan Mihaljevic and Laura Anton-Sanchez.
-- School coordinators.
*** List of courses and brief description ***
* Week 1 (June 23rd - June 27th, 2014) *
1st session: 9:30 - 12:30
Course 1: Bayesian Networks (15 h)
Basics of Bayesian networks. Inference in Bayesian networks. Learning
Bayesian networks from data. Real applications.
Course 2: Time Series(15 h)
Basic concepts in time series. Descriptive methods for time series.
Linear models for time series. Extensions.
2nd session: 13:30 - 16:30
Course 3: Supervised Pattern Recognition (15 h)
Introduction. Assessing the performance of supervised classification
algorithms. Preprocessing. Classification techniques. Combining multiple
classifiers. Comparing supervised classification algorithms.
Course 4: Bayesian Inference (15 h)
Introduction: Bayesian basics. Conjugate models. MCMC and other
simulation methods. Regression and Hierarchical models. Model selection.
3rd session: 17:00 - 20:00
Course 5: Neural Networks and Deep Learning (15 h)
Introduction. Training algorithms. Learning and Optimization. MLPs in
practice. Deep Networks.
Course 6: Feature Subset Selection (15 h)
Introduction. Filter approaches. Wrapper methods. Embedded methods.
Advanced topics. Practical session.
* Week 2 (June 30th - July 4th, 2014) *
1st session: 9:30 - 12:30
Course 7: Statistical Inference(15 h)
Introduction. Some basic statistical test. Multiple testing. Introduction
to bootstrap methods. Introduction to Robust Statistics.
Course 8: Bayesian Classifiers (15 h)
Discrete predictors. Gaussian Bayesian networks-based classifiers. Other
Bayesian classifiers. Bayesian classifiers for: positive and unlabeled data,
semi-supervised learning, data streams, temporal data.
2nd session: 13:30 - 16:30
Course 9: Text Mining (15 h)
Introduction. Fundamentals. Language Modeling. Text Classification.
Information Extraction.
Course 10: Unsupervised Pattern Recognition (15 h)
Introduction to clustering. Data exploration and preparation.
Prototype-based clustering. Density-based clustering. Graph-based clustering.
Cluster evaluation. Miscellanea. Conclusions and final advise.
3rd session: 17:00 - 20:00
Course 11: Support Vector Machines and Convex Optimization (15 h)
Introduction. SVM models. SVM learning algorithms. Convex non
differentiable optimization.
Course 12: Hidden Markov Models (15 h)
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.
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