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Course summary

  • Type MOOC course
  • Period Always open
  • Learning Time 10hr
  • Course approval method Automatic approval
  • Certificate Issue Online
http://kooc.kaist.ac.kr/data-science-programming2
둘러보기
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Instructor Introduction

  • KAIST 산업및시스템공학과 문일철 교수

Lecture plan

강의
  1. CHAPTER 1 Linear Regression 2
    1. 1-1. Matrix Representation of Data1-1
    1. 1-2. Least Square Pricinple of Multivariate Linear Regression
    1. 1-3. Implementation of Multiple Linear Regression
    1. 1-4. Results of Multiple Linear Regression
    1. 1-5. Implementation of Evaluation on Multiple Linear Regression
    1. 1-6. Implementation of Cross Validation
    1. 1-7. Variable Selection
    1. 1-8. Implementation of Simple Variable Selection
    1. 1-9. Informaiton Criterion
    1. 1-10. Stepwise Feature Selection
    1. Lecture Note Chapter 1
    1. Quiz 1
  2. CHAPTER 2 Linear Regression 3
    1. 2-1. API for Linear Regression
    1. 2-2. Linear Regression with Scikit-Learn
    1. 2-3. Variable Selection with Scikit-Learn
    1. 2-4. Additive Regression
    1. 2-5. Implementation of Additive Regression
    1. 2-6. Performance of Additive Regression
    1. Lecture Note Chapter 2
    1. Quiz 2
  3. CHAPTER 3 Machine Learning Frameworks
    1. 3-1. Problems of Linear Regression Inference
    1. 3-2. Linear Regression Revisited
    1. 3-3. Utilizing Automatic Gradient Calculation
    1. 3-4. PyTorch
    1. 3-5. Instantiation of PyTorch Data Structure
    1. 3-6. Dataset and Dataloader of PyTorch
    1. 3-7. Basic Tensor Operations of PyTorch
    1. 3-8. Utilization of GPU for Matrix Calculation
    1. 3-9. Memory Utilization Status
    1. Lecture Note Chapter 3
    1. Quiz 3
  4. CHAPTER 4 Automatic Gradient Calculation
    1. 4-1. Scalar Gradient Calculation
    1. 4-2. Steps of Gradient Calculation by PyTorch
    1. 4-3. PyTorch grad
    1. 4-4. Automatic Parameter Update by PyTorch
    1. 4-5. Case Study of Optimization by PyTorch
    1. 4-6. Linear Regression by Gradient Method in PyTorch
    1. Lecture Note Chapter 4
    1. Quiz 4
  5. CHAPTER 5 Dimension Reduction
    1. 5-1. Examples of High Dimensional Data
    1. 5-2. Concept of Dimension Reduction
    1. 5-3. Projection to Preserve Information
    1. 5-4. Distance from x to x_v
    1. 5-5. Total Distance from x to x_v
    1. 5-6. Optimization for Dimension Reduction
    1. 5-7. Iterative Dimension Reduction
    1. 5-8. Iterative Dimension Reduction
    1. Lecture Note Chapter 5
    1. Quiz 5
  6. ★강의 수강 후 의견을 부탁드리겠습니다.★
    1. 교수님 강의에 대한 별점을 매겨주세요. 여러분의 의견이 많은 도움이 됩니다:D

Additional Info

본 강좌는 Python3 를 기반으로 진행되는 강좌 입니다.
기초적인 내용부터 시작하기 때문에, 누구나 수강할 수 있는 강좌입니다.
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* 강좌 수료 기준 충족 시 수료증을 제공합니다:)