Lecture 0 2019/02/19 Course Logistics [slides]
Registration: [Google Form]
Lecture 1 2019/02/26 Introduction [slides] (video)
Guest Lecture (R103) [PyTorch Tutorial]
Lecture 2 2019/03/05 Neural Network Basics [slides] (video)
Suggested Readings:
[Linear Algebra]
[Linear Algebra Slides]
[Linear Algebra Quick Review]
A1 2019/03/05 A1: Dialogue Response Selection [A1 pages]
Lecture 3 2019/03/12 Backpropagation [slides] (video)
Word Representation [slides] (video)
Suggested Readings:
[Learning Representations]
[Vector Space Models of Semantics]
[RNNLM: Recurrent Neural Nnetwork Language Model]
[Extensions of RNNLM]
[Optimzation]
Lecture 4 2019/03/19 Recurrent Neural Network [slides] (video)
Basic Attention [slides] (video)
Suggested Readings:
[RNN for Language Understanding]
[RNN for Joint Language Understanding]
[Sequence-to-Sequence Learning]
[Neural Conversational Model]
[Neural Machine Translation with Attention]
[Summarization with Attention]
[Normalization]
A2 2019/03/19 A2: Contextual Embeddings [A2 pages]
Lecture 5 2019/03/26 Word Embeddings [slides] (video)
Contextual Embeddings - ELMo [slides] (video)
Suggested Readings:
[Estimation of Word Representations in Vector Space]
[GloVe: Global Vectors for Word Representation]
[Sequence Tagging with BiLM]
[Learned in Translation: Contextualized Word Vectors]
[ELMo: Embeddings from Language Models]
[More Embeddings]
2019/04/02 Spring Break A1 Due
Lecture 6 2019/04/09 Transformer [slides] (video)
Contextual Embeddings - BERT [slides] (video)
Gating Mechanism [slides] (video)
Suggested readings:
[Contextual Word Representations Introduction]
[Attention is all you need]
[BERT: Pre-training of Bidirectional Transformers]
[GPT: Improving Understanding by Unsupervised Learning]
[Long Short-Term Memory]
[Gated Recurrent Unit]
[More Transformer]
Lecture 7 2019/04/16 Reinforcement Learning Intro [slides] (video)
Basic Q-Learning [slides] (video)
Suggested Readings:
[Reinforcement Learning Intro]
[Stephane Ross' thesis]
[Playing Atari with Deep Reinforcement Learning]
[Deep Reinforcement Learning with Double Q-learning]
[Dueling Network Architectures for Deep Reinforcement Learning]
A3 2019/04/16 A3: RL for Game Playing [A3 pages]
Lecture 8 2019/04/23 Policy Gradient [slides] (video)
Actor-Critic (video)
More about RL [slides] (video) Suggested Readings:
[Asynchronous Methods for Deep Reinforcement Learning]
[Deterministic Policy Gradient Algorithms]
[Continuous Control with Deep Reinforcement Learning]
A2 Due
Lecture 9 2019/04/30 Generative Adversarial Networks [slides] (video)
(Lectured by Prof. Hung-Yi Lee)
Lecture 10 2019/05/07 Convolutional Neural Networks [slides]
A4 2019/05/07 A4: Drawing [A4 pages]
2019/05/14 Break A3 Due
Lecture 11 2019/05/21 Unsupervised Learning [slides]
NLP Examples [slides]
Project Plan [slides]
Special 2019/05/28 Company Workshop Registration: [Google Form]
2019/06/04 Break A4 Due
Lecture 12 2019/06/11 Project Progress Presentation
Course and Career Discussion
Special 2019/06/18 Company Workshop Registration: [Google Form]
Lecture 13 2019/06/25 Final Presentation