Deep Learning for NLP (code: DL4NLP)
Instructor: Eneko Agirre
This course introduces in detail the machinery that makes Deep Learning work for NLP, including the latest transformers and large language models like GPT, BERT and T5. Attendants will be able to understand, modify and apply current and future Deep Learning models. They will learn the inner workings of the models and implement them in Keras.

Student profile: professionals, researchers and students with basic programming and Python experience. Basic math skills (algebra or pre-calculus) are also needed. Although not strictly necessary, we recommend subscribing to Collab Pro for more out of GPUs.

Large Language Models (code: LLM)
Instructor: Oier Lopez de Lacalle
The course will introduce large language models, with special emphasis on adaptation techniques (e.g. in-context learning, few-shot, instruction learning) and ways to align with human preferences. In addition, advanced training techniques such as parallelism, selective architectures and scaling laws are presented.
Participants, in addition to understanding the fundamentals of LLMs and learning advanced training techniques, will gain hands-on experience in applying and working with these models, while addressing biases and ethical concerns.

Student profile: professionals, researchers and students with basic programming and Python experience. Basic math skills (algebra or pre-calculus) are also needed. Although not strictly necessary, we recommend subscribing to Collab Pro for more out of GPUs.

Generative Playground: LLMs made easy (code: GPLLMME)
Instructor: Ander Barrena
The aim of this course is to understand and deploy large language models (LLMs) from a practical perspective, enabling students to gain hands-on experience with these models without coding, with particular emphasis on ethical considerations, including addressing bias in language, responsibly handling sensitive information, and evaluating the deployed models.
Participants will learn how to use proprietary models like GPT-4o and open-source models like LLaMa3 for prompt engineering, creating agents, chatbots, Retrieval Augmented Generation (RAG) models, and other NLP applications.

Student profile: graduate students and professionals from various disciplines (linguistics, journalism, computer science, sociology, etc.) who need to understand and deploy LLMs easily. No coding skills are necessary for the practical content. Although not strictly necessary, the OpenAI ChatGPT Plus subscription plan is advisable to complete some of the labs.

Deep Learning for Speech Processing (code: DL4SP)
Instructor:
This course introduces the main Deep Learning techniques used in state-of-the-art Speech Processing. Participants will learn the fundamental approaches behind key tasks such as automatic speech recognition, speaker recognition, language identification and speaker diarization.
The course will present the main neural network architectures used for speech, including convolutional, recurrent and transformer-based models, as well as common speech representations and training strategies. Through practical examples, attendees will learn how current systems are built and how to apply existing models and toolkits to real-world speech processing tasks.

Student profile: professionals, researchers and students with programming and Python experience. Math and signal processing knowledge (at the level of a BSc in Sciences or Engineering) is also recommended. Although not strictly necessary, we recommend subscribing to Collab Pro for more GPU availability.