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Mon, 14 Mar 2016 17:05:42 +0100
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Dear colleagues,

The Technical University of Madrid (UPM) will once more organize the  
'Madrid UPM Advanced Statistics and Data Mining' summer school. The  
summer school will be held in Boadilla del Monte, near Madrid, from  
June 27th to July 8th. This year's edition comprises 12 week-long  
courses (15 lecture hours each), given during two weeks (six courses  
each week). Attendees may register in each course independently. No  
restrictions, besides those imposed by timetables, apply on the number  
or choice of courses.

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

There is a 25% discount for members of Spanish AEPIA and SEIO societies.

Please, forward 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 27th - July 1st, 2016) *

1st session: 9:45-12:45
Course 1: Bayesian Networks (15 h)
       Basics of Bayesian networks. Inference in Bayesian networks.  
Learning Bayesian networks from data. Real applications. Practical  
demonstration: GeNIe, Weka, Bayesia, R.

Course 2: Time Series(15 h)
       Basic concepts in time series. Descriptive methods for time  
series. Linear models for time series. Extensions. Practical  
demonstration: R.

2nd session: 13:45-16:45
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. Practical demonstration: Weka.

Course 4: Bayesian Inference (15 h)
       Introduction: Bayesian basics. Conjugate models. MCMC and other  
simulation methods. Regression and Hierarchical models. Model  
selection. Practical demonstration: R and WinBugs.

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: 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. Practical session: R.


* Week 2 (July 4th - July 8th, 2016) *

1st session: 9:45-12:45
Course 7: Statistical Inference (15 h)
       Introduction. Some basic statistical test. Multiple testing.  
Introduction to bootstrap methods. Introduction to Robust Statistics.  
Practical demonstration: R.

Course 8: Big Data with Apache Spark (15 h)
       Introduction. Spark framework and APIs. Data processing with  
Spark. Spark streaming. Machine learning with Spark MLlib.

2nd session: 13:45-16:45
Course 9: Text Mining (15 h)
       Introduction. Language Modeling. Text Similarity. Text  
Classification. Information Extraction. Practical session: Python,  
with Jupyter notebooks.

Course 10: Feature Subset Selection (15 h)
       Introduction. Filter approaches. Embedded methods. Wrapper  
methods. Advanced topics. Practical session: R and Weka.

3rd session: 17:00-20:00
Course 11: Support Vector Machines and Regularized Learning (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. Practical session: HTK.

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