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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)

€850- €1,450

Face-to-face

English

Program date: July 7 - 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!

Summer school

Data Science

Harnessing Language Models: Your path to NLP Expert

Course Overview

In an era marked by rapid technological advancements and a growing reliance on digital communication, the role of Natural Language Processing (NLP) and Large Language Models (LLMs) is becoming increasingly pivotal in shaping our future work environments. LLMs have revolutionized the way we interact with, analyze, and derive insights from vast amounts of text data. In this context, mastering the art of working with these models is not just a skill; it’s a strategic advantage that can elevate the productivity of an organization and transform the way we work.

NLP and LLM mastery redefine the digital frontier, opening doors to limitless possibilities

To embark on this journey, we will commence by unraveling the fundamentals of transformers and BERT, demystifying their inner workings to provide you with a solid foundation of how deep learning is used for NLP. As the course progresses, we will delve deeper into the world of Large Language Models (LLMs), focusing on practical applications and hands-on exercises that enable you to harness the power of LLMs.

Course Learning Methods

In this course, students will learn through:

Lectures and Presentations: Engaging lectures and presentations to provide foundational knowledge of NLP concepts, LLMs, and tools.
Hands-On Labs: Practical, hands-on labs and exercises where students can apply their knowledge by working on real-world NLP projects.
Case Studies: In-depth case studies of NLP applications in various industries, allowing students to analyze and understand real-world scenarios.
Real-Time Demos: Live demonstrations of NLP tools and techniques to illustrate practical implementation.

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

You can also expect to develop critical skills such as:

Fine-Tuning LLMs
Few-Shot Learning
Prompt Engineering
Problem Solving

The course aims to equip students with the expertise needed to stay relevant in today’s data-driven landscape and thrive and lead in future work environments where LLMs will undoubtedly play a pivotal role.

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: "Large Language Models: Use Cases & Prompt Engineering""

Develop expertise in crafting precise prompts that effectively extract desired information from Large Language Models (LLMs)
Using LLM to dive into your own documents: how to set your first RAG

Class 5: "Beyond LLM: Fine-tuning & Agents"

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
Agent process: introduction to the React (Reason + Act) paradigm and how to make LLM use tools

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.

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 - 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 “Coding 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

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 who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

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€

850€

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

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

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

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 NOT be recorded; however, the materials provided by the professor will be available for a month after 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.