• KAIST 산업및시스템공학과 문일철 교수

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

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