## Course summary

• Type MOOC course
• Period Always open
• Learning Time 10hr
• Course approval method Automatic approval
• Certificate Not Issued
http://kooc.kaist.ac.kr/data-science-programming2
Thumb up 0 Learner 21 ### 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. 2-5. 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. 4-2. Steps of Gradient Calculation by PyTorch
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