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Data Science

Statistical Machine Learning for Large and Unstructured Data

Bayesian Methods and Modern Computational Techniques for Inference and Analysis of Large Data Sets

17.5h (5 days)

€800 - €1,450

Face to Face

English

Program date: July 14 - July 18, 2025

Early bird deadline: April 15, 2025

Learn more

Statistical Machine Learning for Large and Unstructured Data

Applications for 2025 Summer School programs are now open!

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Summer school

Data Science

Statistical Machine Learning for Large and Unstructured Data

This course covers advanced data analysis methods for large, unstructured datasets, focusing on Bayesian methodology for inference rather than prediction.

Participants will explore probabilistic modeling, high-dimensional regression, and practical applications using R and Python.

Teaching Faculty

An Advanced Data Science course focused on Machine Learning

This course would be of use for the following profiles:

Graduate students looking to build a strong foundation in machine learning.
Professionals or consultants who want to transition into data science and machine learning roles.
Researchers, academics and PhD students seeking to incorporate machine learning methods into their research projects.

Learn cutting-edge techniques for analyzing large, unstructured data

Participants of this course will:

Apply Bayesian approaches for valid inferences, not just predictions.
Understand Bayesian modeling to describe dependencies and quantify uncertainty.
Explore Bayesian methods for inference in high-dimensional regression models.
Study latent variable models for analyzing unstructured data like text.
Learn modern tools for implementing advanced analytical methods through case studies.
Discover applications in Economics, Social Sciences, and Biomedicine, among others.
Gain hands-on experience using R and Python for implementing analytical techniques.

Program Syllabus for Statistical Machine Learning for Large Unstructured Data

Here is a course outline of what you will cover:

Course outline

Intro to Probabilistic Modeling, Bayesian Inference, and Hierarchical Modeling.
Computation for Probabilistic Models I: Markov chain Monte Carlo.
Implementing Complex Bayesian Models and Computational Methods (Stan).
Large data: regression with many covariates I. Penalized likelihood and Bayesian approaches, primer on implementing these methods.
Large data: regression with many covariates II. Bayesian Model Selection. Treatment effects with many variables, Double machine learning, and Bayesian views on topics
Linear treatment effects estimators.
Computation for Probabilistic Models II: Variational Inference, how it works, and how it is implemented in publicly available software.
Unstructured data: the case of text data. Exploratory analysis and early latent variable models.
Unstructured data: the case of text data. Latent Dirichlet allocation, some extensions, and applications of these methods in economics and related discipline.
Implementation of latent variable models used for unstructured data (LDA etc.).

List of References

Below is a list of references that may help you prepare for the course:

Articles and books

The Elements of Statistical Learning. T. Hastie, R. Tibshirani, and J. Friedman. Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2001).
An Introduction to Statistical Learning: with Applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani, Springer Series in Statistics Springer New York Inc., New York, NY, USA, (2021).
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer. Gelman.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson D., Vehtari A. & Rubin, D. B. (2015). Bayesian data analysis 3. Chapman and Hall/CRC.
A series of scientific papers describing recent work by leading researchers in Econometrics, Machine Learning and Statistics.

Software / Hardware

Students are required to have their own laptop or desktop computer.
The course is based on Jupyter Notebooks that can be run in Google Colab.
The practical session will mostly be based on R, but most techniques discussed have a Python implementation as well.

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers.

2

Study in vibrant Barcelona.

3

Learn from world-renowned faculty.

Admissions and requirements

It is the participant’s responsibility to ensure they meet the Admissions Criteria.

Program date: July 14 - July 18, 2025

Early bird deadline: April 15, 2025

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.).
Postgraduate degree or current Master’s/PhD studies related to the course topic.
Relevant professional experience.

Requirements for this Statistical Machine Learning course

Basic knowledge of linear algebra, programming, Python/R for data analysis, and statistical learning is required.
Students who do not have significant programming experience will be admitted provided that they attend the 8h “Computing Bootcamp”.
While a graduate-level background in Statistics, Machine Learning, or Data Science is not mandatory to attend the course, it is highly desirable. Participants with limited experience in these fields are encouraged to register for the “Foundations of Data Science” course.

Apply now

Schedule

Here is your schedule for this edition of BSE Data Science Summer School, Statistical Machine Learning course

Time

14

mon

15

tue

16

wed

17

thu

18

fri

09:00 - 11:00

Lecture

14:30 - 16:00

Practical

Credit transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants not interested in credit transfer will instead receive a Certificate of Attendance free of charge. These Participants will not be graded or assessed during the course.

Fees for 2025

Multiple course discounts are available, see more information about available discounts. Fees for courses in other Summer School programs may vary.

Course

Coding Bootcamp in Python and R (8h day)

Foundations of Data Science

Harnessing Language Models: Your Path to NLP Expert

Statistical Machine Learning for Large and Unstructured Data

Modality

Online

Face to Face

Total Hours

8

17.5

ECTS

0

1

Regular Fee

600€

1,450€

Reduced Fee*

350€

800€

FAQ’s

Need more information? Check out our most commonly asked questions or contact our Admissions Team.

Is accommodation included in the course fee?

Accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will be recorded and videos will be available for a month once the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.