XVI Madrid UPM Machine Learning and Advanced Statistics Summer School (June 19th - June 30th, 2023) - Early registration deadline

[ASDM Summerschool]

Dear colleagues,

We would like to remind you that early registration for the Madrid UPM
Machine Learning and Advanced Statistics summer school finishes on May,
26th (included).

The summer school will be held in Boadilla del Monte, near Madrid, from
June 19th to June 30th. 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 *OPEN*. Extended information on course
programmes, price, venue, accommodation and transport is available at
the school's website:

http://www.dia.fi.upm.es/MLAS

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

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

* Week 1 (June 19th - June 23rd, 2023) *

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: 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 Classification (15 h)
Introduction. Assessing the performance of supervised classification
algorithms. Preprocessing. Classification techniques. Combining multiple
classifiers. Comparing supervised classification algorithms. Practical
demonstration: python.

Course 4: Statistical Inference (15 h) Introduction. Some basic
statistical tests. 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. Learning algorithms. Learning in deep networks.
Deep Learning for Images. Deep Learning for Text. Practical session:
Jupyter notebooks in Python Anaconda with keras and tensorflow.

Course 6: 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.


* Week 2 (June 26th - June 30th, 2023) *

1st session: 9:45-12:45

Course 7: Feature Subset Selection (15 h) Introduction. Filter
approaches. Embedded methods. Wrapper methods. Additional topics.
Practical session: R and python.

Course 8: Clustering (15 h) Introduction to clustering. Data exploration
and preparation. Prototype-based clustering. Density-based clustering.
Graph-based clustering. Cluster evaluation. Miscellanea. Conclusions and
final advice. Practical session: R.

2nd session: 13:45-16:45 Course 9: Gaussian Processes and Bayesian
Optimization (15 h) Introduction to Gaussian processes. Sparse Gaussian
processes. Deep Gaussian processes. Introduction to Bayesian
optimization. Bayesian optimization in complex scenarios. Practical
demonstration: python using GPytorch and BOTorch.

Course 10: Explainable Machine Learning (15 h) Introduction. Inherently
interpretable models. Post-hoc interpretation of black box models.
Basics of causal inference. Model-specific explanations: Bayesian
networks. Other topics. Practical demonstration: R.

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 with proximal
methods. Practical session: Python Anaconda with scikit-learn.

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.