Event  Date  Description  Course Materials  

Lecture  Jan 9  Introduction to NLP and Deep Learning [slides] 
Suggested Readings:  
Lecture  Jan 11  Word Vectors 1 [slides] 
Suggested Readings:  
A1 released  Jan 11  Assignment #1 released  [Assignment #1][ Written Solutions ]  
Lecture  Jan 16  Word Vectors 2 [slides] 
Suggested Readings:  
Lecture  Jan 18  Neural Networks [slides] 
Suggested Readings:  
Review  Jan 19  Python Refresher  [ slides ]  
Lecture  Jan 23  Backpropagation and Project Advice [slides] [lecture notes] 
Suggested Readings:  
Lecture  Jan 25  Introduction to TensorFlow [slides] [lecture code] 
Suggested Readings:  
A1 Due  Jan 25  Assignment #1 due  
A2 Released  Jan 25  Assignment #2 released  [Assignment #2] [ Written Solutions ]  
Lecture  Jan 30  Dependency Parsing [slides] 
Suggested Readings:


Lecture  Feb 1  Recurrent Neural Networks and Language Models [slides] 
Suggested Readings: [Ngram Language Models and Perplexity] [The Unreasonable Effectiveness of Recurrent Neural Networks] [Recurrent Neural Networks Tutorial] [Sequence Modeling: Recurrent and Recursive Neural Nets] 

DFP Released  Feb 1  Default Final Project released  
Lecture  Feb 6  Vanishing Gradients, Fancy RNNs [slides] 
Suggested Readings: [Understanding LSTM Networks] [Vanishing Gradients Example] 

Review  Feb 8  Midterm Review [slides] 
This year's midterm will be most similar to practice midterm 3 (the first two are from cs224d).
[practice midterm 1] [with solutions] [practice midterm 2] [with solutions] [practice midterm 3] [with solutions] 

Project Proposal Due  Feb 8  Final Project proposal due  Final Project Proposal  
A2 Due  Feb 8  Assignment #2 due  
Alternate Midterm  Feb 9  Alternate Midterm  
A3 Released  Feb 13  Assignment #3 released  Assignment #3  
Midterm  Feb 13  Inclass midterm 
Location: Memorial Auditorium, Time: 4:30  5:50pm [Midterm] [Midterm Solutions] 

Lecture  Feb 15  Machine Translation, Seq2Seq and Attention [slides] 
Suggested Readings: [Statistical Machine Translation slides (see lectures 2/3/4)] [Statistical Machine Translation Book] [BLEU metric] [Original sequencetosequence NMT paper (also describes beam search)] [Earlier sequencetosequence speech recognition paper (includes detailed beam search alg)] [Original sequencetosequence + attention paper] [Guide to attention and other RNN augmentations] [Massive Exploration of Neural Machine Translation Architectures] 

Lecture  Feb 20  Advanced Attention [slides] 
Suggested Readings: [A Deep Reinforced Model for Abstractive Summarization] [Get To The Point: Summarization with PointerGenerator Networks] [BlackOut: Speeding up Recurrent Neural Network Language Models with very Large Vocabularies] [Achieving Open Vocabulary Neural Machine Translation with Hybrid WordCharacter Models] [QuasiRecurrent Neural Networks] 

Lecture  Feb 22  Transformer Networks and CNNs [slides] 
Suggested Readings: [Attention Is All You Need] [Layer Normalization] [Convolutional Neural Networks for Sentence Classification] [Improving neural network3s by preventing coadaptation of feature detectors] [A Convolutional Neural Network for Modelling Sentences] 

Lecture  Feb 27  Coreference Resolution  
A3 Due  Feb 27  Assignment #3 due  
Milestone Due  Feb 28  Final project milestone due  Project Milestone  
Lecture  Mar 1  Tree Recursive Neural Networks and Constituency Parsing  
Lecture  Mar 6  Advanced Topics TBD  
Lecture  Mar 8  Reinforcement Learning for NLP Guest Lecture  
Lecture  Mar 13  Semisupervised and Multitask Learning  
Lecture  Mar 15  Future of NLP Models, Multitask Learning and QA Systems  
Final Project Due  Mar 16  Final project due  
Poster Presentation  Mar 21  Final project poster presentations 5:308:30 
McCaw Hall at the Alumni Center 