Schedule and Syllabus

Thursday 9:10-12:10 (BL Building 113) [pdf]

Event Date Description Course Material
Lecture 09/22/2016 Introduction Slides: [Course Logistics] [Introduction]
Lecture 09/29/2016 Neural Network Suggested Readings:
1. [Linear Algebra] [Linear Algebra Slides]
2. [Linear Algebra Quick Review]
Slides: [NN Basics]
Lecture 10/06/2016 Backpropagation
Word Representation
Suggested Readings:
1. [Learning Representations by Backpropogating Errors]
2. [From Frequency to Meaning: Vector Space Models of Semantics]
Slides: [Backpropagation] [Word Representation 1]
Lecture 10/13/2016 Word Representation Suggested Readings:
1. [Distributed Representations of Words and Phrases and their Compositionality]
2. [Efficient Estimation of Word Representations in Vector Space]
3. [GloVe: Global Vectors for Word Representation]
Slides: [Word Representation 2] [TensorFlow Tutorial]
A1 Release 10/13/2016 Word Embedding [A1 Page] [A1 Slides]
Lecture 10/20/2016 Sequence Modeling
Recursive Neural Network
Suggested Readings:
1. [Parsing with Compositional Vector Grammars]
2. [Subgradient Methods for Structure Prediction]
3. [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank]
Slides: [Sequence Modeling] [Recursive NN]
Lecture 10/27/2016 Recurrent Neural Network Suggested Readings:
1. [RNNLM: Recurrent Neural Nnetwork Language Model]
2. [Extensions of RNNLM]
3. [RNN for Language Understanding]
4. [RNN for Joint Language Understanding]
5. [Sequence-to-Sequence Learning]
6. [Neural Conversational Model]
Slides: [Recurrent NN 1]
A2 Release 10/27/2016 Sentiment Analysis [A2 Page] [A2 Slides]
A1 Due 10/30/2016 Word Embedding
Guest Lecture 11/03/2016 Guest Lecture by HTC
Convolutional Neural Network
Slides: [Convolutional NN]
Lecture 11/10/2016 NN Practical Tips
Recurrent Neural Network
Suggested Readings:
1. [Initialize Recurrent Networks of Rectified Linear Units]
2. [Practical recommendations for gradient-based training of deep architectures]
Slides: [Company Event] [NN Practical Tips] [Recurrent NN 2]
A3 Release 11/10/2016 Natural Language Understanding [A3 Page] [A3 Slides]
Suggested Readings:
1. [RNN for Language Understanding]
2. [RNN for Joint Language Understanding]
A2 Due 11/17/2016 Sentiment Analysis
Special 11/17/2016 Company Workshop by Yahoo!

[Yahoo! Data Engineering Project Intern]
[Yahoo! Data Scientist Full Time Employee]
[Confirmed Registration List]
09:00 - 09:30 Event Checkin
09:30 - 09:40 Welcome Intro
09:40 - 10:20 Data Engineering Team Sharing
10:30 - 11:10 Product Engineering Team Sharing
11:10 - 11:40 Search Team Sharing
11:40 - 12:00 Group Photo and Good Bye
Lecture 11/24/2016 Gating Mechanism
Attention and Memory
Suggested Readings:
1. [Long Short-Term Memory]
2. [Gated Recurrent Unit]
3. [Neural Machine Translation with Attention]
4. [Summarization with Attention]
Slides: [Gating Mechanism] [Attention and Memory]
Project Announcement 11/24/2016 P1: Dialogue State Tracking Challenge
P2: Machine Comprehension
[P1 Page] [P1 Slides]
[P2 Page] [P2 Slides]
Lecture 12/01/2016 Deep Reinforcement Learning Suggested Readings:
1. [Stephane Ross’ thesis (Introduction)]
Slides: [Deep Reinforcement Learning 1]
A3 Due 12/01/2016 Natural Language Understanding
A4 Release 12/01/2016 Language Generation [A4 Page] [A4 Slides]
Suggested Readings:
1. [Sequence to Sequence Machine Translation]
2. [Semantically Conditioned Natural Language Generation] [Semantically Conditioned NLG Slides]
Lecture 12/08/2016 Deep Reinforcement Learning Suggested Readings:
1. [Playing Atari with Deep Reinforcement Learning]
2. [Continuous Control with Deep Reinforcement Learning]
Slides: [Deep Reinforcement Learning 2]
A5 Release 12/08/2016 Game Playing [A5 Page] [A5 Slides]
A4 Due 12/15/2016 Language Generation
Special 12/15/2016 Company Workshop by Microsoft [Confirmed Registration List]
09:00 - 09:30 Opening
09:30 - 10:00 Internship and Employment Program Sharing
10:00 - 10:30 Future Vision of Technology
10:30 - 11:30 Tech Talk (Machine Learning)
11:30 - 12:00 Company Tour
Lecture 12/22/2016 Unsupervised Learning Suggested Readings:
1. [Generative Adversarial Nets]
2. [Deep Convolutional Generative Adversarial Nets]
Slides: [Dialogue System] [Machine Comprehension] [Unsupervised Learning]
Lecture 12/29/2016 Generative Model/Deep Learning Trends Suggested Readings:
1. [Conditional GAN]
2. [Adversarially Learned Inference]
3. [Improved Techniques for Training GANs]
4. [Unrolled GANs]
5. [Generative Adversarial Parallelization]
Slides: [Generative Model] [Deep Learning Trends] [Review]
A5 Due 12/29/2016 Game Playing
Special 01/05/2016 Break
Project Due 01/08/2017 Dialogue State Tracking Challenge and Machine Comprehension
Project Presentation 01/12/2017 Final Project Oral Presentation