Artificial Intelligence – All in One

Lecture 116 — Word Similarity and Thesaurus Methods — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 115 — WordNet and Other Online Thesauri — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 114 — Word Senses and Word Relations — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 113 — Evaluating Search Engines — [ NLP || Christopher Manning || Stanford University ]

Lecture 112 — Calculating TF IDF Cosine Scores — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 111 — The Vector Space Model 1— [ NLP || Christopher Manning || Stanford University ]

Lecture 110 — TF IDF Weighting — [ NLP || Christopher Manning || Stanford University ]

Lecture 109 — Inverse Document Frequency Weighting — [ NLP || Christopher Manning || Stanford ]

Lecture 108 — Term Frequency Weighting — [ NLP || Christopher Manning || Stanford University ]

Lecture 107 — Scoring with the Jaccard Coefficient — [ NLP || Christopher Manning || Stanford ]

Lecture 106 — Introducing Ranked Retrieval — [ NLP || Christopher Manning || Stanford University ]

Lecture 105 — Phrase Queries and Positional Indexes — [ NLP || Christopher Manning || Stanford ]

Lecture 104 — Query Processing with the Inverted Index — [ NLP || Christopher Manning || Stanford ]

Lecture 103 — The Inverted Index — [ NLP || Christopher Manning || Stanford University ]

Lecture 102 — Term Document Incidence Matrices — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 101 —Introduction to Information Retrieval — [ NLP || Christopher Manning || Stanford ]

Lecture 100 — Dependencies Encode Relational Structure — [ NLP || Christopher Manning || Stanford ]

Lecture 99 — Greedy Transition Based Parsing — [ NLP || Christopher Manning || Stanford University ]

Lecture 97 — Latent Variable PCFGs — [ NLP || Christopher Manning || Stanford University ]

Lecture 96 — The Return of Unlexicalized PCFGs — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 95 — PCFG Independence Assumptions — [ NLP || Christopher Manning || Stanford University ]

Lecture 94 — Charniaks Model — [ NLP || Christopher Manning || Stanford University ]

Lecture 93 — Lexicalization of PCFGs — [ NLP || Christopher Manning || Stanford University ]

Lecture 92 — Constituency Parser Evaluation — [ NLP || Christopher Manning || Stanford University ]

Lecture 91— CKY Example — [ NLP || Christopher Manning || Stanford University ]

Lecture 90 — CKY Parsing — [ NLP || Christopher Manning || Stanford University ]

Lecture 89 — Grammar Transforms — [ NLP || Christopher Manning || Stanford University ]

Lecture 88 — CFGs and PCFGs — [ NLP || Christopher Manning || Stanford University ]

Lecture 87 — The Exponential Problem in Parsing — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 86 — Empirical Data Driven Approach to Parsing — [ NLP || Christopher Manning ]

Lecture 85 — Syntactic Structure Constituency vs Dependency — [ NLP || Christopher Manning ]

Lecture 84 — Some Methods and Results on Sequence Models for POS Tagging — [ NLP || Chris Manning ]

Lecture 83 — An Intro to Parts of Speech and POS Tagging — [ NLP || Christopher Manning ]

Lecture 78 — Semi Supervised and Unsupervised Relation Extraction — [ NLP || Dan Jurafsky ]

Lecture 77 — Supervised Relation Extraction — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 76 — Using Patterns to Extract Relations — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 75 — What is Relation Extraction — — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 74 — Maximum Entropy Sequence Models — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 73 — Sequence Models for Named Entity Recognition — [ NLP || Christopher Manning]

Lecture 72 — Evaluation of Named Entity Recognition — [ NLP || Christopher Manning || Stanford ]

Lecture 71 — Introduction to Information Extraction — [ NLP || Christopher Manning || Stanford ]

Lecture 70 — Maximizing the Likelihood — [ NLP || Christopher Manning || Stanford University ]

Lecture 69 — Generative vs Discriminative models The problem of overcounting evidence — [ NLP ]

Lecture 68 — Building a Maxent Model The Nuts and Bolts — [ NLP || Christopher Manning ]

Lecture 67 — Feature Based Linear Classifiers — [ NLP || Christopher Manning || Stanford Univ ]

Lecture 66 — Making features from text for discriminative NLP models — [ NLP || Chrisopher Manning ]

Lecture 65 — Generative vs Discriminative Models — [ NLP || Christopher Manning || Stanford ]

Lecture 64 — Other Sentiment Tasks — [ NLP || Dan Jurafsky || Stanford University ]

Lecture 63 — Learning Sentiment Lexicons — [ NLP || Dan Jurafsky || Stanford University ]