Supervised Learning

Supervised learning is a fundamental paradigm in machine learning where an algorithm learns from a dataset that has been manually labeled with the correct outputs or answers. The core idea is to train a model on these input-output pairs, allowing it to learn a mapping function that can generalize and make accurate predictions on new, unseen data for which the output is unknown. This approach is broadly categorized into two main types of problems: classification, where the goal is to predict a discrete category (e.g., identifying an email as spam or not spam), and regression, where the goal is to predict a continuous value (e.g., forecasting a house price).

1. Foundations of Supervised Learning
   1. Defining Supervised Learning
      1. Core Definition and Characteristics
      2. Comparison with Unsupervised Learning
      3. Comparison with Reinforcement Learning
      4. The Role of Labeled Data
         1. Definition of Labels
         2. Sources of Labeled Data
            1. Manual Annotation
            2. Automated Labeling Systems
            3. Domain Expert Labeling
            4. Crowdsourced Labeling
         3. Label Quality and Consistency
            1. Inter-annotator Agreement
            2. Label Noise and Errors
            3. Quality Control Measures
      5. Input Features and Target Variables
         1. Types of Input Features
            1. Numerical Features
               1. Continuous Variables
               2. Discrete Variables
            2. Categorical Features
               1. Nominal Categories
               2. Binary Categories
            3. Ordinal Features
            4. Text Features
            5. Image Features
            6. Time Series Features
         2. Target Variable Types
            1. Discrete Targets
               1. Binary Targets
               2. Multiclass Targets
               3. Multilabel Targets
            2. Continuous Targets
               1. Bounded Continuous
               2. Unbounded Continuous
      6. The Goal of Generalization
         1. Definition of Generalization
         2. Training Error vs Generalization Error
         3. Overfitting vs Underfitting
            1. Signs of Overfitting
            2. Signs of Underfitting
            3. Causes and Prevention
         4. Importance in Real-World Applications
   2. Core Terminology and Concepts
      1. Model or Hypothesis
         1. Definition of a Model
         2. Hypothesis Space
         3. Model Complexity
         4. Parametric vs Non-parametric Models
      2. Training, Validation, and Test Sets
         1. Purpose of Each Dataset
         2. Data Splitting Strategies
            1. Random Splitting
            2. Stratified Splitting
            3. Time-based Splitting
         3. Typical Split Ratios
      3. Parameters and Hyperparameters
         1. Model Parameters
            1. Learned Parameters
            2. Parameter Estimation
         2. Hyperparameters
            1. Definition and Examples
            2. Impact on Model Performance
         3. Examples in Common Algorithms
      4. Cost Function or Loss Function
         1. Purpose of Loss Functions
         2. Properties of Good Loss Functions
         3. Common Loss Functions
            1. Mean Squared Error
            2. Mean Absolute Error
            3. Cross-Entropy Loss
            4. Hinge Loss
            5. Huber Loss
      5. Optimization and Learning
         1. Gradient Descent
            1. Concept of Optimization
            2. Mathematical Foundation
            3. Learning Rate
               1. Fixed Learning Rate
               2. Adaptive Learning Rate
               3. Learning Rate Scheduling
            4. Variants of Gradient Descent
               1. Batch Gradient Descent
               2. Stochastic Gradient Descent
               3. Mini-batch Gradient Descent
         2. Other Optimization Methods
            1. Newton's Method
            2. Quasi-Newton Methods
            3. Evolutionary Algorithms
   3. Types of Supervised Learning Problems
      1. Classification Problems
         1. Binary Classification
            1. Definition and Examples
            2. Decision Thresholds
            3. Class Imbalance Issues
         2. Multiclass Classification
            1. Definition and Examples
            2. One-vs-Rest Strategy
            3. One-vs-One Strategy
            4. Direct Multiclass Methods
         3. Multilabel Classification
            1. Definition and Examples
            2. Difference from Multiclass
            3. Problem Transformation Methods
            4. Algorithm Adaptation Methods
            5. Evaluation Challenges
      2. Regression Problems
         1. Definition and Examples
         2. Continuous Output Prediction
         3. Linear vs Nonlinear Regression
         4. Univariate vs Multivariate Regression
      3. Structured Prediction
         1. Sequence Labeling
         2. Object Detection
         3. Semantic Segmentation