ML

My repository for machine learning note-taking and review

Table of Contents

1. Basic Concepts
   • Bias-Variance Tradeoff
   • Underfitting vs. Overfitting
     • Cross-Validation
     • Fixing High Bias (Underfitting)
     • Fixing High Variance (Overfitting)
   • Ensemble Methods
   • Loss Functions
     • Loss Functions for Classification
     • Loss Functions for Regression
     • MLE vs. Loss Functions
     • Batch Learning vs. Online Learning
   • Confusion Matrix
     • Accuracy
     • Recall
     • Precision
   • ROC Curves, PR Curves

2. Machine Learning Algorithm

   • ML Problem Settings
     • Basic Assumptions and Definitions
     • Optimization (Gradient Descent)
   • Deterministic & Probabilistic Problems
   • Paramertric vs. Nonparametric Models
     • Parametric Models
     • Nonparametric Models
   • Diagnose ML model
   • Supervised Learning
     1. Decision Tree (DT)
        • Random Forest (RF)
        • Boosting Regression Trees (Gradient Boosting)
        • Boosting Classification Trees (AdaBoost)
     2. KNN (K-Nearest Neighbors) Classification
     3. Perceptron
     4. Linear Regression
        • Regularization
          • Ridge Regression (L2 Regularization)
          • Lasso Regression (L1 Regularization)
     5. Logistic Regression
     6. Neural Network
     7. Naive Bayes
     8. Support Vector Machine (SVM)
     9. K-Means Clustering
     10. Time Series Forecasting

3. Practical Data Science Techniques

4. ML Questions