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Data Science

Harnessing Language Models: Your Path to NLP Expert

Explore transformers and BERT basics, then advance to hands-on applications and work with Large Language Models.

17.5h (5 days)

€800- €1,450

Face to Face

English

Program date: July 7 - July 11, 2025

Early bird deadline: April 15, 2025

Learn more

Harnessing Language Models: Your Path to NLP Expert

Applications for 2025 Summer School programs are now open!

Apply

Summer school

Data Science

Harnessing Language Models

Discover the fundamentals of transformers and BERT to build a solid foundation in deep learning for NLP. You’ll then explore Large Language Models (LLMs) through practical applications and hands-on exercises to harness their power effectively.

Teaching Faculty

In the digital age, NLP and LLM expertise are the keys to innovation and success. Want to learn more?

This course has been designed for:

Graduate students and professionals looking to build a strong foundation in BERT and other language models.

The course equips students to stay relevant in today's data-driven landscape and lead in future work environments where LLMs will play a pivotal role.

Upon successful completion of the course, you will have acquired a comprehensive understanding of:

The most important concepts of NLP and how LLMs compare to other NLP methods.
Transformers and BERTS and it’s practical applications.
Large Language Models (LLMs) fundamentals, their roles in NLP, and practical uses as sentiment analysis, language translation, and text summarization.
Hugging Face’s cutting-edge tools, libraries, and APIs will empower you to harness the full potential of LLMs for tasks like text generation, question-answering, and more.
Identify and apply NLP and LLM techniques in real-world scenarios.
Ethical considerations surrounding LLMs is critical in today’s data-driven world.

Program Syllabus for BSE Natural Language Processing Course

Here is a course outline of what you will cover:

Class 1: "Foundations of NLP and LLMs"

An introductory overview of NLP methods, including key concepts and comparisons with other NLP techniques. Discussion of ethical concerns.

Class 2: "Transformers, BERT, and Large Language Models"

A deep dive into transformer architecture, with a particular focus on BERT and its practical applications in NLP. Explore the fundamentals and practical roles of Large Language Models (LLMs).

Class 3: "Few-Shot Learning and Hugging Face Tools"

Learn how to efficiently apply few-shot learning techniques for fast problem-solving in NLP. Gain proficiency in using Hugging Face’s tools, libraries, and APIs for tasks related to Large Language Models (LLMs).

Class 4: "Prompt Engineering for LLMs"

Develop expertise in crafting precise prompts that effectively extract desired information from Large Language Models (LLMs).

Class 5: "Fine-Tuning LLMs and Adaptation"

Master the art of customizing pre-trained Large Language Models (LLMs) for specific tasks and domains. Learn how to adapt these models to your unique data and requirements.

List of References

To help you prepare for this course, we recommend the following texts:

Recommended texts

Vaswani et al., (2017), Attention Is All You Need.
Devlin et al., (2018) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
Alamar, Jay, (2018) The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning).
Alamar, Jay, (2018) The Illustrated Transformer.
Wolf et al. (2019) HuggingFace’s Transformers: State-of-the-art Natural Language Processing.
Sun et al., (2019), How to Fine-Tune BERT for Text Classification?
Brown et al., (2020), Language Models are Few-Shot Learners.
Gao, Tianyu, (2021), Prompting: Better Ways of Using Language Models for NLP Tasks.
Timo Schick and Hinrich Schütze (2021). Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference.
Bender et al., (2021), On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Strubell et al. (2019) Energy and Policy Considerations for Deep Learning in NLP.
Dodge et al. (2022) Measuring the Carbon Intensity of AI in Cloud Instances.
Sheng et al. (2019) The Woman Worked as a Babysitter: On Biases in Language Generation.

Software / Hardware

Students are required to have their own laptop or desktop computer and a stable Internet connection to fully engage in and benefit from the course.

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 7 - July 11, 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 language models course

Participants of this course are expected to be familiar with the fundamentals of linear algebra and programming with Python and R.
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, Harnessing Language Models Your Path to NLP Expert, course.

Time

7

mon

8

tue

9

wed

10

thu

11

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

10

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?

Unfortunately, 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.