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Statistical Learning Laboratory
20. Midterm Exam
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Statistical Learning Laboratory
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Lectures
Lectures
Linear algebra
Linear algebra
Module 1 - Vector and Matrix
Module 1 - Vector and Matrix
1. Introduction
2. Vector and Matrix 1
3. Vector and Matrix 2
4. Vector and Matrix 3
5. Vector and Matrix 4
6. Vector and Matrix 5
7. Vector and Matrix 6
8. Vector and Matrix 7
9. Exercise
Module 2 - System of Linear Equations
Module 2 - System of Linear Equations
10. System of Linear Equation 1
11. System of Linear Equation 2
12. System of Linear Equation 3
13. System of Linear Equation 4
14. System of Linear Equation 5
15. System of Linear Equation 6
16. System of Linear Equation 7
17. System of Linear Equation 8
18. System of Linear Equation 9
19. Exercise
20. Midterm Exam
Module 3 - Determinants
Module 3 - Determinants
21. Determinant 1
22. Determinant 2
23. Determinant 3
24. Determinant 4
25. Exercise
Module 4 - Vector Spaces
Module 4 - Vector Spaces
26. Vector space 1
27. Vector space 2
28. Vector space 3
29. Exercise
30. Exercise
31. Semester Exam
Module 5 - Eigenvalues and Decomposition
Module 5 - Eigenvalues and Decomposition
32. Eigen value and vector 1
33. Eigen value and vector 2
34. Eigen value and vector 3
35. Eigen value and vector 4
36. Eigen value and vector 5
37. Exercise
38. Midterm Exam
39. Singular value decomposition 1
40. Singular value decomposition 2
Module 6 - Applications
Module 6 - Applications
41. Principal component analysis 1
42. Principal component analysis 2
43. Principal component analysis 3
44. Factor analysis 1
45. Factor analysis 2
46. Nonlinear dimension reduction
47. Wrap up
48. Semester Exam
Data Science without syntax
Data Science without syntax
Foundations
Foundations
1. Getting-Started
2. Data Visualization
3. Data Wrangling
4. Data Import and Tidy Data
Regression Analysis
Regression Analysis
5. Simple Linear Regression
6. Multiple Regression
Statistical Methods
Statistical Methods
7. Sampling Method
8. Estimation, CI and Bootstrapping
9. Hypothesis Testing
10. Inference for Regression
Communications
Communications
11. Tell Your Story with Data
Statistics and Probability
Statistics and Probability
Research
Research
Decision Trees and Forests
Decision Trees and Forests
Generalized random forests
Variable Importance Measures
Random forest kernels
Causal Inference
Causal Inference
Sparse Causal BART
Causal mediation analysis
Causal data repository
Factor analysis
Factor analysis
Theoretical Foundations
Theoretical Foundations
1. はじめに
2. 因子とは
3. 因子負荷行列
4. 潜在因子推定法
Analysis Methods
Analysis Methods
5. 回転基準と結果の解釈
6. 感度分析
7. 因子分析の手順
Applications
Applications
8. シミュレーション
9. 糖尿病潜在原因分析
10. 順序ありカテゴリカル変数の扱い
11. 飛行機乗客満足度分析
Technical Notes
Technical Notes
12. 行列分解と因子分析
線形代数学 I / 基礎 / II - 第20回 中間試験
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