Dear colleagues,
We would like to remind you that early registration for the Madrid UPM
Advanced Statistics and Data Mining summer school is open until June
6th. 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.
For the second time in a row, INOMICS has selected our summer school
as one of the world's top ten summer schools in mathematics and
statistics. You can read more at http://bit.ly/1TweJjj
Early registration is *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. Practical session: Python with keras
and Jupyter notebooks.
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. 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.
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