Natural Language Processing

16 lectures

From linguistic structure to modern sequence models for understanding and generating text.

01Lecture 01 · Introduction to Natural Language Processing

02Lecture 02 · Vector Semantics and Word Embeddings

03Lecture 03 · Word Embeddings and Word2Vec

04Lecture 04 · CBOW, GloVe, and Statistical Language Modeling

05Lecture 05 · Perplexity and Neural Language Models

06Lecture 06 · Large Language Models, Prompt Engineering, and Part-of-Speech Tagging

07Lecture 07 · Part-of-Speech Tagging: Viterbi, MEMM, and Neural Approaches

08Lecture 08 · Mid-Semester Exam Revision: NLP Foundations

09Lecture 09 · Parsing — From Ambiguity to Parse Trees

10Lecture 10 · Dependency Parsing

11Lecture 11 · Contextual Word Embedding and Attention Mechanisms

12Lecture 12 · Contextual Word Embedding and Word Sense Disambiguation

13Lecture 13 · Knowledge Graphs and Semantic Web

14Lecture 14 · Retrieval Augmented Generation

15Lecture 15 · Text Summarization

16Lecture 16 · Final Recap: Parsing, Contextual Embeddings, Word Sense, Retrieval Augmented Generation and Text Summarization

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