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PART I (진행중)

회차	일시	내용	발표자	발표자료
1	2/9	(NLP기초) Week One : Introduction 1/2	송치성
		(ML기초) 1. Introduction & Linear Regression with One Variable	김단비
		(DeepRL) Introduction and course overview	김무성
2	2/23	(NLP기초) Week Two: Introduction 2/2
		(NLP실습) python, docker 환경설정	김무성	도커환경설정, 파이썬3기초
		(ML기초) 2. Linear Regression with Multiple Variables
		(DeepRL) Supervised learning and decision making
3	3/9	(NLP기초) Week Three: NLP Tasks and Text Similarity (1)
		(NLP실습) nltk, konlpy, 은전한닢
		(ML기초) 3. Logistic Regression
		(DeepRL) Optimal control and planning
4	3/23	(NLP기초) Week Three: NLP Tasks and Text Similarity (2)
		(NLP실습)
		(ML기초) 4. Neural Networks: Representation
		(DeepRL) Review section: autodiff, backpropagation, optimization
5	4/6	(NLP기초) Week Four: Syntax and Parsing, Part 1 (1)
		(NLP실습)
		(ML기초) 5. Neural Networks: Learning
		(DeepRL) Learning dynamical system models from data
6	4/20	(NLP기초) Week Four: Syntax and Parsing, Part 1 (2)
		(NLP실습)
		(ML기초) 6. Advice for Applying Machine Learning
		(DeepRL) Learning policies by imitating optimal controllers
7	5/4	(NLP기초) Week Five: Syntax and Parsing, Part 2
		(NLP실습)
		(ML기초) 7. Machine Learning System Design
		(DeepRL) Guest lecture: Igor Mordatch, OpenAI