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September 25, 2023

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Description

CS224U: Natural Language Understanding. Instructors: Prof. Christopher Potts, Prof. Bill MacCartney, and Omar Khattab, Department of Linguistics and Computer Science, Stanford University.

From conversational agents to automated trading and search queries, natural language understanding underpins many of today's most exciting technologies. How do we build these models to understand language efficiently and reliably? In this project-oriented course, you will develop systems and algorithms for robust machine understanding of human language. The course draws on theoretical concepts from linguistics, natural language processing, and machine learning. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Course Curriculum

  • Lecture 01 – Course Overview Unlimited
  • Lecture 02 – Homework 1: Word Relatedness Unlimited
  • Lecture 03 – Distributed Word Representations: High-Level Goals and Hypotheses Unlimited
  • Lecture 04 – Distributed Word Representations: Matrix Designs Unlimited
  • Lecture 05 – Distributed Word Representations: Vector Comparison Unlimited
  • Lecture 06 – Distributed Word Representations: Basic Reweighting Unlimited
  • Lecture 07 – Distributed Word Representations: Dimensionality Reduction Unlimited
  • Lecture 08 – Distributed Word Representations: Retrofitting Unlimited
  • Lecture 09 – Distributed Word Representations: Static Representations from Contextual Models Unlimited
  • Lecture 10 – Homework 2: Sentiment Analysis Unlimited
  • Lecture 11 – Overview of Supervised Sentiment Analysis Unlimited
  • Lecture 12 – Supervised Sentiment Analysis: General Practical Tips Unlimited
  • Lecture 13 – Supervised Sentiment Analysis: Stanford Sentiment Treebank Unlimited
  • Lecture 14 – Supervised Sentiment Analysis: DynaSent Unlimited
  • Lecture 15 – Supervised Sentiment Analysis: sst.py Unlimited
  • Lecture 16 – Supervised Sentiment Analysis: Hyperparameter Search Unlimited
  • Lecture 17 – Supervised Sentiment Analysis: Feature Representation Unlimited
  • Lecture 18 – Supervised Sentiment Analysis: RNN Classifiers Unlimited
  • Lecture 19 – Contextual Representation Models Unlimited
  • Lecture 20 – Contextual Word Representations: Transformers Unlimited
  • Lecture 21 – Contextual Word Representations: BERT Unlimited
  • Lecture 22 – Contextual Word Representations: RoBERTa Unlimited
  • Lecture 23 – Contextual Word Representations: ELECTRA Unlimited
  • Lecture 24 – Contextual Word Representations: Practical Fine-tuning Unlimited
  • Lecture 25 – Homework 3: Colors Unlimited
  • Lecture 26 – Grounded Language Understanding Unlimited
  • Lecture 27 – Grounded Language Understanding: Speakers Unlimited
  • Lecture 28 – Grounded Language Understanding: Listeners Unlimited
  • Lecture 29 – Grounded Language Understanding: Varieties of Contextual Grounding Unlimited
  • Lecture 30 – Grounded Language Understanding: The Rational Speech Acts Model Unlimited
  • Lecture 30 – Grounded Language Understanding: The Rational Speech Acts Model Unlimited
  • Lecture 31 – Grounded Language Understanding: Neural RSA Unlimited
  • Lecture 32 – Natural Language Inference: Overview Unlimited
  • Lecture 33 – Natural Language Inference: SNLI, MultiNLI, and Adversarial NLI Unlimited
  • Lecture 34 – Natural Language Inference: Adversarial Testing Unlimited
  • Lecture 35 – Natural Language Inference: Modeling Strategies Unlimited
  • Lecture 36 – Natural Language Inference: Attention Unlimited
  • Lecture 37 – NLU and Information Retrieval: Overview Unlimited
  • Lecture 38 – NLU and Information Retrieval: Classical IR Unlimited
  • Lecture 39 – NLU and Information Retrieval: Neural IR, Part 1 Unlimited
  • Lecture 40 – NLU and Information Retrieval: Neural IR, Part 2 Unlimited
  • Lecture 41 – NLU and Information Retrieval: Neural IR, Part 3 Unlimited
  • Lecture 42 – Relation Extraction: Overview Unlimited
  • Lecture 43 – Relation Extraction: Data Resources Unlimited
  • Lecture 44 – Relation Extraction: Problem Formulation Unlimited
  • Lecture 45 – Relation Extraction: Evaluation Unlimited
  • Lecture 46 – Relation Extraction: Simple Baselines Unlimited
  • Lecture 47 – Relation Extraction: Directions to Explore Unlimited
  • Lecture 48 – Overview of Analysis Methods in NLP Unlimited
  • Lecture 49 – Analysis Methods in NLP: Adversarial Testing Unlimited
  • Lecture 50 – Analysis Methods in NLP: Adversarial Training (and Testing) Unlimited
  • Lecture 51 – Analysis Methods in NLP: Probing Unlimited
  • Lecture 52 – Analysis Methods in NLP: Feature Attribution Unlimited
  • Lecture 53 – Overview of Methods and Metrics Unlimited
  • Lecture 54 – Methods and Metrics: Classifier Metrics Unlimited
  • Lecture 55 – Methods and Metrics: Natural Language Generation Metrics Unlimited
  • Lecture 56 – Methods and Metrics: Data Organization Unlimited
  • Lecture 57 – Methods and Metrics: Model Evaluation Unlimited
  • Lecture 58 – Presenting Your Work: Final Papers Unlimited
  • Lecture 59 – Writing NLP Papers Unlimited
  • Lecture 60 – NLP Conference Submissions Unlimited
  • Lecture 61 – Giving Talks Unlimited
  • Lecture 62 – Conclusions Unlimited

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