Machine learning

Notes on Machine Learning

This collection of links serves as a comprehensive guide to various aspects of machine learning, from foundational concepts to advanced techniques and frameworks.

Machine Learning Overview

Core Concepts

Model Development and Evaluation

  • Optimisation Techniques: Algorithms to improve model performance, including both gradient-based and gradient-free options.
    • Gradient Descent, Stochastic Gradient Descent, Stochastic Gradient descent with momentum, Mini-Batch Gradient Descent, Adagrad, RMSProp, AdaDelta, Adam, Gradient-free optimisation
  • Model Selection and Evaluation: Strategies for selecting the best model and assessing its performance.
  • Error and Performance Metrics: Metrics to evaluate model errors and performance.

Machine Learning Methods and Techniques

Machine Learning Applications and Frameworks

Fundamental Theory and Statistics