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Deep learning

Paper Reading Roadmap

본 포스팅은 고려대학교 산업경영공학부 Data Science & Business Analytics 연구실강필성 교수님의 자료를 정리한 포스팅입니다.


Contents of Posting

Paper Reading Roadmap

ML Basics

  • The matrix calculus you need for deep learning
  • Statistical Modeling: The Two Cultures
  • Machine learning: Trends, perspectives, and prospects
  • An introduction to ROC analysis
  • Learning from imbalanced data
  • Variational inference: A review for statisticians
  • The expectation-maximization algorithm
  • Dimension Reduction: A Guided Tour

Data Mining

General

Patter Mining

Clustering

Artificial Intelligence

General

Reinforcement Learning

Transfer Learning

Supervised Learning

Kernel Machines

Ensemble

Semi-supervvised Learning

Unsupervised Learning

Neural Network

General

Structure

Learning Strategies

NLP

General

Topic Modeling

  • An introduction to latent semantic analysis
  • Probabilistic latent semantic analysis
  • Probabilistic topic models
  • Latent Dirichlet Allocation

Repersentation Learning

Classification

Summarization

Machine Translation

Question Answering

Vision

Classification

Object Detection

Localization & Segmentation

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