Statistical Learning Laboratory

31. Semester 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
- Causal Inference
  Causal Inference
- 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