[ABE-L] Fwd: Thirteenth Madrid UPM Advanced Statistics and Data Mining Summer School (June 25th - July 6th, 2018)

Seg Mar 12 16:08:00 -03 2018

---------- Forwarded message ----------
From: <asdm em fi.upm.es>
Date: 2018-03-12 14:31 GMT-03:00
Subject: Thirteenth Madrid UPM Advanced Statistics and Data Mining Summer
School (June 25th - July 6th, 2018)
To: allstat em jiscmail.ac.uk

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 25th to
July 6th. 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.

Our summer school has been an INOMICS world top ten summer schools in
mathematics and statistics from 2015 to 2018. See the last year's ranking
at http://bit.ly/2oR00GI

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 Larrañaga, Concha Bielza, Bojan Mihaljević and Santiago Gil Begué.
-- School coordinators.

*** List of courses and brief description ***

* Week 1 (June 25th - June 29th, 2018) *

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. Linear models for time series. Time
series clustering. 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: Statistical Inference (15 h)
      Introduction. Some basic statistical test. Multiple testing.
Introduction to bootstrap methods. Introduction to Robust Statistics.
Practical demonstration: R.

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. Practical session: Python with keras and Jupyter
notebooks.

Course 6: Big Data with Apache Spark (15 h)
      Introduction. Spark framework and APIs. Data processing with Spark.
Spark streaming. Machine learning with Spark MLlib.

* Week 2 (July 2nd - July 6th, 2018) *

1st session: 9:45-12:45
Course 7: 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.

Course 8: 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.

2nd session: 13:45-16:45
Course 9: Text Mining (15 h)
      Information Retrieval 101. Unsupervised Text Processing.
Representation Learning. Information Extraction. Natural Language
Understanding. Practical session: Python, with Jupyter notebooks.

Course 10: Feature Subset Selection (15 h)
      Introduction. Filter approaches. Embedded methods. Wrapper methods.
Additional 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. Regularized
learning. Convex optimization for regularized learning. Practical session:
Python with scikit-learn, Jupyter notebooks.

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.

You may leave the list at any time by sending the command

SIGNOFF allstat

to listserv em jiscmail.ac.uk, leaving the subject line blank.

-- 
Basilio de Bragança Pereira,MSc and DL(COPPE) DIC and PhD(Imperial
College).
UFRJ- Universidade Federal do Rio de Janeiro
Professor Emérito
(Bioestatística -Faculdade de Medicina e Estatística Aplicada -COPPE).
Coordenador Substituto do Programa de Pós Graduação de Cardiologia
*ICES -Instituto do Coração Edson Saad , HUCFF-Hospital Universitário
Clementino Fraga Filho.
*Tel: 55 21 3938-7045/6225

http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783624H9
<http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783624H9>
http://www.poscardio.ufrj.br/index.php/pt-BR/
http://www.coppe.ufrj.br/pt-br/programas/engenharia-de-producao

*Mail Address:
Programa de Engenharia de Produção -
COPPE/UFRJ - Caixa Postal 68507
CEP 21941-972 Rio de Janeiro,RJ
Brazil

The best thing about being a statistician is that you get to play in
everyone´s backyard (John Tukey)
. <http://www.azquotes.com/quote/603405>
O artista vê a verdade no belo , o cientista vê o belo na verdade.
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