Date Topic Section I: Introduction / background Lecture 1 (Jan 11) Introduction to NLP Lecture 2 Basics of Deep Learning: Backpropagation and Neural Networks Section II: NLP core techniques Lecture 3 Language Modeling and Recurrent Neural Networks Lecture 4 Word Meaning and Word Embedding Lecture 5 Sentence Embeddings, Convolutional Neural Networks Lecture 6 & 7 Graph Representations for NLP, Graph Convolutional Network Lecture 8 Machine Translation, Seq2Seq and Attention Lecture 9 Transformer and BERT Lecture 10 Pre-trained Language Models (student mini lectures) Lecture 11 Constituency Parsing Lecture 12 Syntactic Dependency Parsing Section III: Cutting-edge research topics. Lecture 13 Data, Knowledge, and Logic: Modeling and Reasoning for Natural Language Understanding Lecture 14 Guest lecture, TBD Lecture 15 Knowledge Graph Lecture 16 & 17 Conference tutorial, TBD Lecture 18 & 19 Conference tutorial, TBD Lecture 20 & 21 Conference tutorial, TBD Lecture 22 & 23 Course project presentations and discussions.