updates

7 files changed, 32 insertions, 45 deletions

diff --git a/SI/.obsidian/app.json b/SI/.obsidian/app.json
index 9fc5682..eaddc23 100644
--- a/SI/.obsidian/app.json
+++ b/SI/.obsidian/app.json
@@ -21,5 +21,6 @@
   "propertiesInDocument": "source",
   "spellcheck": true,
   "autoPairMarkdown": false,
-  "strictLineBreaks": false
+  "strictLineBreaks": false,
+  "useMarkdownLinks": true
 }
\ No newline at end of file
diff --git a/SI/.obsidian/plugins/recent-files-obsidian/data.json b/SI/.obsidian/plugins/recent-files-obsidian/data.json
index 8627ee1..ca80f7c 100644
--- a/SI/.obsidian/plugins/recent-files-obsidian/data.json
+++ b/SI/.obsidian/plugins/recent-files-obsidian/data.json
@@ -1,14 +1,18 @@
 {
   "recentFiles": [
     {
-      "basename": "Dashboard",
-      "path": "Dashboard.md"
+      "basename": "Bias and Variance",
+      "path": "Resource/Data Science/Machine Learning/Contents/Bias and Variance.md"
     },
     {
       "basename": "Machine Learning",
       "path": "Resource/Data Science/Machine Learning/Machine Learning.md"
     },
     {
+      "basename": "Dashboard",
+      "path": "Dashboard.md"
+    },
+    {
       "basename": "Gradient descent",
       "path": "Resource/Data Science/Machine Learning/Contents/Gradient descent.md"
     },
@@ -17,10 +21,6 @@
       "path": "Resource/Data Science/Machine Learning/Contents/Classification.md"
     },
     {
-      "basename": "Bias and Variance",
-      "path": "Resource/Data Science/Machine Learning/Contents/Bias and Variance.md"
-    },
-    {
       "basename": "SAA",
       "path": "Resource/AWS/SAA.md"
     },
diff --git a/SI/.obsidian/workspace.json b/SI/.obsidian/workspace.json
index 64b655c..6bbcaab 100644
--- a/SI/.obsidian/workspace.json
+++ b/SI/.obsidian/workspace.json
@@ -13,13 +13,13 @@
             "state": {
               "type": "markdown",
               "state": {
-                "file": "Dashboard.md",
+                "file": "Resource/Data Science/Machine Learning/Contents/Bias and Variance.md",
                 "mode": "source",
                 "backlinks": false,
                 "source": true
               },
               "icon": "lucide-file",
-              "title": "Dashboard"
+              "title": "Bias and Variance"
             }
           }
         ]
@@ -77,8 +77,7 @@
               "title": "Recent Files"
             }
           }
-        ],
-        "currentTab": 1
+        ]
       }
     ],
     "direction": "horizontal",
@@ -195,14 +194,14 @@
       "periodic-notes:Open today": false
     }
   },
-  "active": "b1f7f5ee0151b994",
+  "active": "3d54370a0282bec3",
   "lastOpenFiles": [
+    "Resource/Data Science/Machine Learning/Machine Learning.md",
+    "Dashboard.md",
     "Resource/Data Science/Machine Learning/Contents/Gradient descent.md",
     "Resource/Data Science/Machine Learning/Contents/Classification.md",
     "Resource/Data Science/Machine Learning/Contents/Bias and Variance.md",
     "Resource/AWS/SAA.md",
-    "Dashboard.md",
-    "Resource/Data Science/Machine Learning/Machine Learning.md",
     "Resource/AWS",
     "Resource/Data Science/SQL/MySQL/MySQL.md",
     "Spaces/Home/Archive/Data_Science",
diff --git a/SI/Dashboard.md b/SI/Dashboard.md
index ea9865e..dcb8a56 100644
--- a/SI/Dashboard.md
+++ b/SI/Dashboard.md
@@ -9,32 +9,27 @@ cssclasses:
   - dashboard
   - dashboard-ReadLineLength
 ---
-
 # Dashboard
 
 - ### 🏠 [House]()
-
   - 💰 Budget
   - [[Q1 2024]]
   - #### 🛒 Grocery
   - 💳 Transaction
-
 - ### 👤 [Personal]()
-
   - #### 🏡[Archive](file:////Users/si/Documents/SI/Archive)
   - #### ✍️ [Area](file:////Users/si/Documents/SI/Area)
   - #### 📁 [Projects](file:////Users/si/Documents/SI/Project)
   - #### 📚 [Resource](file:////Users/si/Documents/SI/Resource)
   - ✅ [To-do](file:////Users/si/Documents/SI/To-do)
-    `$=dv.list(dv.pages('"To-do"').sort(f=>f.file.name,"desc").limit(4).file.link)`
+	`$=dv.list(dv.pages('"To-do"').sort(f=>f.file.name,"desc").limit(4).file.link)`
 
 - ### 🏢 [School]()
-
   - 📔 [Class]()
-    `$=dv.list(dv.pages('"Resource"').sort(f=>f.file.mtime.ts,"desc").limit(4).file.link)`
+	`$=dv.list(dv.pages('"Resource"').sort(f=>f.file.mtime.ts,"desc").limit(4).file.link)`
   - 💼 [Project]()
   - ✏️ [Assignment]()
-    `$=dv.list(dv.pages('#assignment').sort(f=>f.file.mtime.ts,"desc").file.link)`
+	`$=dv.list(dv.pages('#assignment').sort(f=>f.file.mtime.ts,"desc").file.link)`
 
 - ### 🚧 Life Progress
 
diff --git a/SI/Notes/1735275859-resource.md b/SI/Notes/1735275859-resource.md
deleted file mode 100644
index 0638325..0000000
--- a/SI/Notes/1735275859-resource.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-id: 1735275859-resource
-aliases:
-  - Resource
-tags: []
----
-
-# Resource
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