⚙️ Machine Learning Notes
Contents
- 01 Supervised Learning
- 02 Optimisation in Logistic Regression
- 03 Nonlinear Transformation in Machine Learning
- 04 Support Vector Machines
- 05 Lagrange Duality and SVM Dual Formulation
- 06 Support Vector Machines - Dual Predictions & Kernels
- 07 Soft Margin SVM - Primal and Dual Formulations
- 08 Sequential Minimal Optimisation (SMO)
- 09 Linear Regression
- 10 Bayesian Inference and Probabilistic Linear Regression
- 11 Bayesian Linear Regression
- 12 Learning Feasibility
- 13 VC Dimension and Generalisation
- 14 Bias and Variance Analysis
- 15 Overfitting and Regression Model Evaluation
- 16 Regularisation
- 17 Validation