Alexander Rush and Yoon Kim, Harvard University
Time: Tues/Thurs 1:002:30pm
Location: Pierce 209
Date  Location  Topic  Materials 

Sep. 1, 1011am (Mark)  Pierce 301  Math Review (Linear Algebra, Calculus, Probabilistic Theory)  
Sep. 4, 56pm (Zhirui)  Pierce 301  Math Review (Linear Algebra, Calculus, Probabilistic Theory)  
Sep. 7, 56pm (Rachit)  Pierce 320  Code Review (Python, Numpy, Matplotlib, PyTorch)  
Sep. 8, 1111:59am (Rachit)  MD 223  Code Review (Python, Numpy, Matplotlib, PyTorch) 
The ideal outcome of this project would be a paper that could be submitted to a toptier natural language or machine learning conference such as ACL, EMNLP, NIPS, ICML, or UAI. There are different ways to approach this project, which are dis cussed in a more comprehensive document that is available on the course website. There are four separate components of the project.
You will upload these materials via Canvas. Please see the syllabus (linked in the course website) for a more thorough description of the final project and policies related to collaboration, etc.
Date  Due  Descriptions 

March 22  Abstract and Status Report  This is a three to four page document that contains a draft of your final abstract, as well as a brief status report on the progress of your project. 
May 3  Talk Session  You will make a conferencestyle talk about your project. Talks are 7 minutes long and limited to 3 slides. 
May 9  Final Report  You will write a report of up to ten pages, in the style of a mainstream CS conference paper. Please use the provided template (see here) 
Our syllabus this semester consists of two parts. The first part of the semester will be an accelerated background on applied deep learning for natural language processing with a series of Kaggle competitions. The second part of the semester will consist of student led paper presentations on the topic of deep probabilistic sequence modeling with latent variables.
Date  Area  Topic  Demos  Required Readings  Assignment (DUE: Tues in class) 

Jan. 23  Sequence Classification  Intro BagofWords 


Jan. 25  Convolutions 


Jan. 30  Sequence Modeling  NNLMs 

Classification (Kaggle)  
Feb. 1  RNNs 


Feb. 6  Sequence Transduction  Encoding 


Feb. 8  Attention 


Feb. 13  Search 

Modeling (Kaggle)  
Feb. 15  REINFORCE 


Feb. 20  Latent Variable Models of Text  Variational Inference/Variational Autoencoders 


Feb. 22  Generative Adversarial Networks 


Feb. 27.  NLP Topics  Problem and Datasets 

Translation (Kaggle)  
Mar. 1  Guest Lecture  Marc'Aurelio Renzato (Facebook)  Structured Training for NMT  MD G125 4pm  
Mar. 6.  Midterm 


Mar. 8  Projects  Discussion Signup 11am3pm 


Mar. 20  Student Groups  Conditional VAEs (Justin, Yoon) 


Mar. 22  Logic Programming / Differentiable Theory Search 

Final Project Abstracts  
Mar. 27  Model Bias / Variational NMT 


Mar. 29  ImagetoText / TexttoImage 

Latent Variables  
Apr. 3  Unsupervised MT 


Apr. 5  Discrete GANs 


Apr. 10  Speech 


Apr. 12  Neural Discourse and Pragmatics 


Apr. 17  Reading Comp. / Summary 


Apr. 19  Bio / Style Transfer 


Apr. 24  Conclusion 


May 3rd  Final Presentation Talks (Evening)  
May 9th  Final Paper Due 