updates

2 files changed, 15 insertions, 15 deletions

diff --git a/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md b/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md
index 294f138..a3e6398 100644
--- a/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md
+++ b/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md
@@ -34,9 +34,9 @@ tags:
 
 - Solution
   - Use validation data set
-    - $\bbox[teal,5px,border:2px solid red]{\text{Train data (80\%)+ Valid data (10\%) + Test data (10\%)}}$
-    - Cannot directly participate in model training
-    - Continuously evaluates in the learning base, and stores the best existing performance
+  - $\bbox[teal,5px,border:2px solid red]{\text{Train data (80\%)+ Valid data (10\%) + Test data (10\%)}}$
+  - Cannot directly participate in model training
+  - Continuously evaluates in the learning base, and stores the best existing performance
   - K-fold cross validation
     - **Leave-One-Out Cross-Validation (LOOCV)**
       - a special case of k-fold cross-validation where **K** is equal to the number of data points in the dataset.
diff --git a/SI/Resource/Data Science/Machine Learning/Machine Learning.md b/SI/Resource/Data Science/Machine Learning/Machine Learning.md
index 6dbb5e8..3ee6924 100644
--- a/SI/Resource/Data Science/Machine Learning/Machine Learning.md
+++ b/SI/Resource/Data Science/Machine Learning/Machine Learning.md
@@ -32,7 +32,7 @@ Learning is to find the best model represented data, meaning optimization of par
   - A model with the smallest difference between predictions $\hat{y}$ and actual values $y$
   - A model parameter makes the smallest loss
 
-## Types of learning
+## Types of Learning
 
 ### Supervised Learning
 
@@ -46,9 +46,9 @@ Learning is to find the best model represented data, meaning optimization of par
    - [[Support Vector Machine]] ([[Support Vector Machine |SVM]])
    - [[Decision Tree]]
    - [[Linear Discriminant Analysis]] ([[Linear Discriminant Analysis |LDA]])
-     1. [[Ensemble]]
-     - [[Bagging]]
-     - [[Boosting]]
+   1. [[Ensemble]]
+   - [[Bagging]]
+   - [[Boosting]]
 
 ### Unsupervised Learning
 
@@ -65,11 +65,11 @@ Learning is to find the best model represented data, meaning optimization of par
 
 - Data Properties
   - Features (= attributes, independent variables): X
-    - characteristics of data or items
-    - N: # of data sample
-    - D: # of features
+  - characteristics of data or items
+  - N: # of data sample
+  - D: # of features
   - Label (dependent variables): y
-    - if there is a label, it is supervised. Otherwise, it is unsupervised
+  - if there is a label, it is supervised. Otherwise, it is unsupervised
   - Parameter (=weight): learnable parameters that a model have, not given data
   - [[Hyperparameter]]: parameters that human has to decide
 - Input vs. Output
@@ -77,11 +77,11 @@ Learning is to find the best model represented data, meaning optimization of par
   - Output ($\hat{y}$): values of prediction derived from model
 - Linear vs. Nonlinear
   - Linear regression: a model can be implemented by a linear function
-    - Simple Linear Regression: Involves two variables — one independent variable and one dependent variable. The relationship between these variables is modeled as a straight line.
-    - Multiple Linear Regression: Uses more than one independent variable to predict a dependent variable. The relationship is still linear in nature, meaning it assumes a straight-line relationship between each independent variable and the dependent variable.
-    - ex) $y = w_0 + w_1*x_1 + w_2*x_2 + \dots + w_D*x_D, y = w_0 + w_1*x_1 + w_2*x^2$
+  - Simple Linear Regression: Involves two variables — one independent variable and one dependent variable. The relationship between these variables is modeled as a straight line.
+  - Multiple Linear Regression: Uses more than one independent variable to predict a dependent variable. The relationship is still linear in nature, meaning it assumes a straight-line relationship between each independent variable and the dependent variable.
+  - ex) $y = w_0 + w_1*x_1 + w_2*x_2 + \dots + w_D*x_D, y = w_0 + w_1*x_1 + w_2*x^2$
   - Non-linear regression: a model can't be implemented by a linear function
-    - ex) $log(y) = w_0 + w_1*log(x), y = max(x, 0)$
+  - ex) $log(y) = w_0 + w_1*log(x), y = max(x, 0)$
 
 ## Basic Math for ML