Machine Learning Algorithms

   February 20, 2025
  1. Introduction
    1. The Role of Machine Learning in Modern Technology: Need, Definition, and Its Interdisciplinary Impact
    2. Machine Learning: What It Is, How It Works, and Its Types
    3. Challenges of Machine Learning
    4. Machine Learning Process
    5. Machine Learning Applications
  2. Data Analytics and Visualization
    • Data and Types of Data
    • Data Analytics
    • Data Visualization
    • Feature Engineering
    • Dimensionality Reduction
  3. Basic Learning Theory
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  4. Supervised Learning
    • Regression and classification problems,
    • Simple linear regression,
    • Multiple linear regression,
    • Ridge regression,
    • Logistic regression,
    • K-nearest neighbour,
    • Naive Bayes classifier,
    • Linear discriminant analysis,
    • Support vector machine,
    • Decision trees,
    • Bias variance trade-off,
    • Cross-validation methods
      • Leave-one-out (LOO) cross-validation,
      • K-folds cross-validation,
    • Multi-layer perceptron,
    • Feed-forward neural network
  5. Unsupervised Learning
    • clustering algorithms:
      • k-means/k-medoid,
      • hierarchical clustering,
    • top-down, bottom-up:
      • singlelinkage, multiple-linkage,
    • dimensionality reduction,
      • principal component analysis.
  6. Semi Supervised Learning
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  7. Artificial Neural Network
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  8. Reinforcement Learning
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