Spring 2017 CS292F Syllabus

• 04/04 Introduction, logistics, NLP, and deep learning.
• 04/06 Tips for a successful class project
• 04/11 Word embeddings (HW1 out)
  • A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning, Collobert and Weston, ICML 2008
  • Distributed Representations of Words and Phrases and their Compositionality, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, NIPS 2013
  • Glove: Global Vectors for Word Representation, J Pennington, R Socher, CD Manning - EMNLP, 2014
• 04/13 Knowledge base embeddings
  • A three-way model for collective learning on multi-relational data, M Nickel, V Tresp, HP Kriegel, ICML 2011
  • Translating embeddings for modeling multi-relational data, A Bordes, N Usunier, A Garcia-Duran, NIPS 2013
  • A Review of Relational Machine Learning for Knowledge Graphs, Nichel et al., Proceedings of the IEEE
• 04/18 Neural network basics (Project proposal due)
• 04/20 Neural networks language models
• 04/25 RNNs (HW1 due and HW2 out)
• 04/27 LSTMs/GRUs
  • Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Cho et al., EMNLP 2014
  • Sequence to Sequence Learning with Neural Networks, Sutskever et al., NIPS 2014
• 05/02 Convolutional Neural Networks (1)
  • Natural Language Processing (Almost) from Scratch, Collobert et al., JMLR 2011
  • Convolutional Neural Networks for Sentence Classification, Yoon Kim, EMNLP 2014
• 05/04 Convolutional Neural Networks (2)
• 05/09 Project: mid-term presentation (1)
• 05/11 Project: mid-term presentation (2) (HW2 due)
• 05/16 Language and vision
• 05/18 Information extraction
• 05/23 Speech recognition and understanding
• 05/25 Sequence-to-sequence models and neural machine translation
• 05/30 Attention mechanisms in NLP
• 06/01 Question answering
• 06/06 Project: final presentation (1)
• 06/08 Project: final presentation (2)