Dear colleagues,
The Technical University of Madrid (UPM) will once more organize the
summer school on 'Advanced Statistics and Data Mining'. The summer
school will be held in Madrid, between June 23th and July 4th. This
year's programme comprises 12 courses divided into 2 weeks. Attendees
may register in each course independently.
Early registration is now *OPEN*. 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 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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