그래프 이론

0. 강의계획
- 수업목표:
- 담당교수: 이창기 (한빛관 301호, leeck@kangwon.ac.kr)
- Office hours: Tuesday PM 3:00 ~ 4:00
- 수업운영방식: 강의 및 프로젝트
- 성적평가방식: 중간고사 40% + 기말고사 50% + 출석/기타 20%
1. Introduction to Graph Theory [PDF]
2-1. Graph-based Algorithms [PDF]
2-2. Graph-based Algorithms (Max-flow and min-cut) [PDF] (taken from https://www.cs.princeton.edu/courses/archive/spring13/cos423)
3. Graph-based IR - HITS, PageRank [PDF]
4-1. Graph-based NLP [PDF]
4-2. Graph-based NLP (Dependency Parsing) [PDF]
5. Node Embedding [PDF] (taken from Representation Learning on Networks, snap.stanford.edu/proj/embeddings-www, WWW 2018)
6. KB Embedding [PDF]
7-1. Word Embedding (taken from http://web.stanford.edu/class/cs224n) [PDF]
7-2. Advanced Word Embedding (taken from http://web.stanford.edu/class/cs224n) [PDF]
9-1. Recurrent Neural Networks (taken from http://web.stanford.edu/class/cs224n) [PDF]
9-2. Fancy Recurrent Neural Networks for Machine Translation (taken from http://web.stanford.edu/class/cs224n) [PDF]