194.025 Introduction to Machine Learning

• Machine Learning
• Learning Paradigms:
  • Supervised Learning
    • Classification
    • Regression
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Self-Supervised Learning
  • Reinforcement Learning
  • Transfer Learning
  • Active Learning
  • Passive Learning
  • Online Learning
  • Batch Learning
  • Representation Learning
• Learning Methods:
  • Distance-based Learning
    • Manhattan Distance
    • Euclidean Distance
    • Minkowski Distance
    • Levenshtein Distance
    • Cosine Similarity
  • Linear Regression
  • Polynomial Regression
  • Multiple Polynomial Regression
  • Linear Classification
    • Binary Linear Classifier
    • Perceptron
    • Kernel Perceptron
    • Logistic Regression
    • Support Vector Machine (SVM)
  • Decision Trees
  • Bayesian Learning
  • Multiclass Classification
• Kernel Methods:
  • Kernel Function
  • Gram Matrix
  • Hinge Loss
  • Common Kernels:
    • Linear Kernel
    • Polynomial Kernel
    • Gaussian RBF Kernel
  • Ensemble Learning
    • Bagging
    • Boosting
  • Artificial Neural Networks (ANN)
    • Multi-Layer Perceptron (MLP)
    • Deep Neural Networks
    • Activation Functions
      • Sigmoid
      • tanh
      • Rectified Linear Unit (ReLU)
    • Backpropagation
    • Cross-Entropy Loss
    • Universal Function Approximation
    • Transformers
• Dimensionality Reduction:
  • Dimensionality Reduction
• Risk and Evaluation:
  • ML Evaluation
  • Empirical Risk
  • True Risk
  • Underfitting
  • Overfitting
  • Bias-Variance Tradeoff
  • Dataset Splitting
  • k-fold Cross-Validation
  • Baselines
  • Student-t Test
  • Metrics:
    • MAE
    • RMSE
    • Confusion Matrix
    • Accuracy
    • Precision
    • Sensitivity
    • F1-Score
• Probabilistic Machine Learning:
  • Bayes Optimal Classifier
  • Discriminative Learning
  • Generative Learning
  • Inference and Estimation:
    • Maximum Likelihood Estimation (MLE)
    • Maximum a Posteriori Estimation (MAP)
    • Posterior Predictive Distribution
  • Probabilistic Graphical Models:
    • Bayesian Network
    • Naive Bayes
• Optimisation:
  • Gradient Descent
  • Gradient Checking
  • Normal Equations
  • Regularisation
    • L1 Regularisation
    • L2 Regularisation
  • Hyperparameters
  • Hyperparameter Tuning
• Fundamental Assumptions:
  • I.I.D. Assumption
  • Realisability Assumption
  • Cluster Assumption
  • Manifold Assumption
• Learning Theory:
  • Hypothesis Class
  • Consistent Hypothesis
  • PAC Learning
  • PAC-Learnable Class
  • Sample Complexity
  • Generalisation Bound
  • Rademacher Complexity
  • Shattering
  • VC Dimension
• Mathematical Foundations:
  • Eigenvalue
  • Eigenvector
  • Convex Function
  • Basis Function
  • Lagrange Multipliers
  • Dual Problem
  • KL Divergence
• Graph Representations:
  • Adjacency List
  • Adjacency Matrix
  • Adjacency Set
  • Weisfeiler-Leman Algorithm
  • Weisfeiler-Leman Graph Kernel
• Bias and Fairness:
  • Ethical AI
• Data Analysis:
  • Data Modality
  • Exploratory Data Analysis (EDA)
  • Feature Engineering
• Data Preprocessing:
  • Independent Feature Scaling
  • Min-Max Scaling
  • Mean Normalisation
  • Standardisation
  • One-Hot Encoding
  • Imputation
• Data Types:
  • Numerical Data
  • Categorical Data
    • Nominal Data
    • Ordinal Data
  • Image Data
  • Text Data