Sparse Communication: Transformations and Applications

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Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Coreference Resolution

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Compositional Representations and Systematic Generalization

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Introduction to Natural Language Processing

Covers the basics of Natural Language Processing, from traditional to modern approaches, highlighting the challenges and importance of studying both methods.