16th Machine Learning and Advanced Statistics Summer School (formerly ASDM) 2023

Madrid UPM Advanced Statistics and Data Mining Summer School Enrollment is now OPEN!

Madrid - June 19th to June 30th, 2023
Organized by the Artificial Intelligence Department of the School of Computer Science of the Universidad Politécnica de Madrid, with the participation of some members of the ELLIS Unit Madrid.

The summer school consists of 12 separate courses, each of them week-long and composed of 15 lecture hours. There are six courses in each of the two weeks of the summer school. The courses are intensive and aim to introduce both the theoretical foundations and the practical applications of modern statistical analysis techniques. The students are free to choose the courses according to their interests, with the only restriction being that courses which are given in the same time block (e.g. C01 and C02; C03 and C04;…) are mutually exclusive.

Click on a course title to see its detailed programme and eventual prerequisites.

TimeWeek 1
Monday, June 19th – Friday, June 23rd, 2023
Week 2
Monday, June 26th – Friday, June 30th, 2023
9:45 – 12:45

C01Bayesian Networks 
Concha Bielza, Pedro Larrañaga, Bojan Mihaljević (Univ. Politécnica de Madrid)

C02Time Series 
Ana Justel, Karen Miranda (Univ. Autónoma de Madrid), Andrés Alonso (Univ. Carlos III de Madrid)

C07Feature Subset Selection 
Bojan Mihaljević, Pedro Larrañaga, Concha Bielza (Univ. Politécnica de Madrid)

Abraham Otero (Univ. CEU San Pablo)

13:45 – 16:45

C03Supervised Classification
Pedro Larrañaga, Concha Bielza, Bojan Mihaljević (Univ. Politécnica de Madrid)

C04Statistical Inference 
Román Mínguez (Univ. de Castilla-La Mancha)

C09Gaussian Processes and  Bayesian Optimization

Daniel Hernández-Lobato (Univ. Autónoma de Madrid), Eduardo Garrido (Univ. Pontificia Comillas)

C10Explainable Machine Learning
Bojan Mihaljević, Enrique Valero-Leal (Univ. Politécnica de Madrid)

17:00 – 20:00

C05Neural Networks and Deep Learning 
Álvaro Barbero, Alberto Suárez (Univ. Autónoma de Madrid)

C06Bayesian Inference 
Concepción Ausín (Univ. Carlos III de Madrid)

C11Support Vector Machines and Regularized Learning
Álvaro Barbero, Carlos Alaíz (Univ. Autónoma de Madrid)

C12Hidden Markov Models 
Agustín Álvarez (Univ. Politécnica de Madrid)

All classes will be given in English. Each course has theoretical as well as practical hours, in which the each techniques are put into practice with software. Students should bring their own laptops with the software required for the practical sessions. Free wireless connection will be available. A leaflet summarizing the information is available here.

Goals and prerequisites

Academic interest: This summer school complements the background of students from a variety of disciplines with the theoretical and practical fundamentals of those modern techniques employed in the analysis and modelling of large data sets.

Scientific interest: Any scientist, regardless of her field (whether engineering, life sciences, economics, etc.) is confronted with the problem of extracting conclusions from experimental data. The summer school supplies experimentalists with the sufficient resources to be able to select the appropriate analysis technique and to apply it to their specific problem.

Professional interest: The application of modern data analysis is widespread in the industry it is practically needed in nearly all disciplines. There are plenty job offers in the field: a Glassdoor.com search as of March 2017 retrieves around 9,000 offers for “data science” and around 14,000 offers for ‘data mining’, all within the USA only.

The goal of this summer school is to complement the technical background of attendees in the field of data analysis and modelling. The courses are open to any student or professional wanting to enrich her knowledge of a topic that is more and more involved in nearly all productive areas (Computer Science, Engineering, Pharmacy, Medicine, Economics, Statistics, etc.).

A second objective is to get the student acquainted with a set of computational tools for applying the learned techniques. This may involve tackling practical problems from the student’s own work environment, i.e., working with a student’s own data set.

Note that the summer school is on advanced techniques and the courses will provide insight into modern techniques that, nearly by definition, are not mathematically trivial. Although emphasis is placed on their use and not on the underlying mathematics, attendees should not be afraid or surprised of seeing some mathematics. Teachers will make the course content accessible to students from all backgrounds. To make course attendance easier, the student is supposed to be familiar with certain concepts that are described as “prerequisites” and she is encouraged to read the “before attending documents” in order to benefit from the course as much as possible.


The tuition fees include attendance to lectures and educational materials. There is an early-registration period until June, 1st at 23:59 hours (CET). The price for each one of the 12 courses is:


By May 26th

After May 26th


275 €

325 €


400 €

450 €

People who are not currently working are eligible for academia fees. Note that the above per-course price is fixed, regardless of how many courses a student enrolls in. There is a 25% discount for members of the Spanish AEPIA and SEIO societies.

Organized by the Artificial Intelligence Department of the School of Computer Science of the Universidad Politécnica de Madrid.

More info and registration at http://dia.fi.upm.es/es/MLAS