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Explore the theoretical foundations and practical applications of natural language understanding through this comprehensive Stanford University course taught by Professor Christopher Potts. Master key concepts from linguistics, natural language processing, and machine learning while diving deep into contextual word representations including Transformers, GPT, BERT, RoBERTa, and ELECTRA architectures. Learn about information retrieval systems, from classical IR approaches to modern neural methods, and discover evaluation metrics for assessing retrieval performance. Examine in-context learning techniques and their evolution from origins to current state-of-the-art methods. Develop skills in behavioral evaluation of NLU models through compositionality testing, adversarial evaluation, and specialized datasets like COGS and DynaSent. Gain expertise in analysis methods including probing techniques, feature attribution, causal abstraction, and Distributed Alignment Search (DAS). Study essential NLP methods and metrics covering classifier evaluation, generation metrics, dataset construction, and model assessment protocols. Learn professional research skills including literature review techniques, experimental protocol design, academic paper writing, conference submission processes, and effective presentation strategies for research talks. Apply domain adaptation techniques for supervised sentiment analysis and explore retrieval-augmented learning approaches through hands-on homework assignments and bake-off competitions that reinforce theoretical concepts with practical implementation experience.
