Machine Learning

1. Introduction:

• Deduction, Abduction & Induction
• Induction in Machine Learning
• Probability Formulae
• Types of Machine Learning
• Model Selection vs Parameter Optimization

2. Supervised Learning - Regression

• Regression
• Squared Error Function
• Empirical Error (Parameter Optimization)
• Regularisation
• Perceptron in Mathematical Notation

3. Probabilitstic Modelling

• Aleatoric vs Epistemic Uncertainty
• Using Likelihood to Model Probability
• Bayesian Inference
• Maximum a-posteriori estimation for a Gaussian distributed data model using regularization.
• MAP = Regularized Least Squares
• Predictive Distribution

4. Incremental Bayesian Learning

• Incremental Bayesian Learning

5. Error Minimization

• Generalized Learning Rule
• Finding Hyper Parameter
• Cross Validation
• Bias-Variance decomposition
• Bias vs Variance
• Double Descent

6. Models

• Linear Learning Models
• Finding Optimum Parameters for Linear Learning Model
• Gradient Descent

7. Definition of Decision Boundary

• Decision Boundary and Hyperplane
• Three approaches to classification
  • Discriminant Function (Fisher LDF as Example)
  • Direct Posterior Modeling

Concept Learning

• Validity of Fundamental Equivalence between Induction and Deduction