Word Embeddings: Introduction and Applications

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Vector Space Semantics (and Information Retrieval)

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Word Embeddings: Glove and Semantic Relationships

Explores word embeddings, Glove model, semantic relationships, subword embeddings, and syntactic relationships.

Compositional Representations and Systematic Generalization

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

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Coreference Resolution

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Question Answering: Deep Learning Insights

Explores question answering systems, reading comprehension models, and the challenges in achieving accurate responses.

Deep Learning: Principles and Applications

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

Natural Language Processing Basics

Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.

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.

Manipulating Objects with Robots: Vision-Language Integration

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.