Optimization is a critical aspect of machine learning, where algorithms strive to find the best solution among a vast number of possible options. In this course, we delve into the fundamental principles and techniques of optimization in machine learning.

Key Topics

  • Gradient Descent: Learn about the most common optimization algorithm used in machine learning.
  • Convergence: Understand the criteria for determining when an optimization algorithm has converged.
  • Hyperparameter Tuning: Explore methods to optimize the hyperparameters of machine learning models.
  • Optimization in Neural Networks: Discover how optimization techniques are applied to neural networks.

Course Outline

  1. Introduction to Optimization

    • Overview of optimization in machine learning
    • Importance of optimization in model performance

  2. Gradient Descent

    • Derivation of the gradient descent algorithm
    • Types of gradient descent (e.g., batch, stochastic, mini-batch)

  3. Convergence Criteria

    • Criteria for determining convergence
    • How to interpret convergence plots

  4. Hyperparameter Tuning

    • Grid search and random search
    • Bayesian optimization

  5. Optimization in Neural Networks

    • Challenges in optimizing neural networks
    • Techniques like momentum and adaptive learning rates

Learn More

For further reading, check out our comprehensive guide on Machine Learning Optimization Techniques.

Gradient Descent