Syllabus

Tuesday 9:10-12:10 (CS Building 104)

Event Date Description Course Material Note
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:
  1. [Linear Algebra]
  2. [Linear Algebra Slides]
  3. [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:
  1. [Learning Representations]
  2. [Vector Space Models of Semantics]
  3. [RNNLM: Recurrent Neural Nnetwork Language Model]
  4. [Extensions of RNNLM]
 [Optimzation]
Lecture 4 2019/03/19 Recurrent Neural Network [slides] (video)
Basic Attention [slides] (video)
 Suggested Readings:
  1. [RNN for Language Understanding]
  2. [RNN for Joint Language Understanding]
  3. [Sequence-to-Sequence Learning]
  4. [Neural Conversational Model]
  5. [Neural Machine Translation with Attention]
  6. [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:
  1. [Estimation of Word Representations in Vector Space]
  2. [GloVe: Global Vectors for Word Representation]
  3. [Sequence Tagging with BiLM]
  4. [Learned in Translation: Contextualized Word Vectors]
  5. [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:
  1. [Contextual Word Representations Introduction]
  2. [Attention is all you need]
  3. [BERT: Pre-training of Bidirectional Transformers]
  4. [GPT: Improving Understanding by Unsupervised Learning]
  5. [Long Short-Term Memory]
  6. [Gated Recurrent Unit]
 [More Transformer]
Lecture 7 2019/04/16 Reinforcement Learning Intro [slides] (video)
Basic Q-Learning [slides] (video)
 Suggested Readings:
  1. [Reinforcement Learning Intro]
  2. [Stephane Ross' thesis]
  3. [Playing Atari with Deep Reinforcement Learning]
  4. [Deep Reinforcement Learning with Double Q-learning]
  5. [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:
  1. [Asynchronous Methods for Deep Reinforcement Learning]
  2. [Deterministic Policy Gradient Algorithms]
  3. [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

What's New?

Feb 13 - Website launch.