Welcome to the Advanced Mathematics for AI course! 🧠 This program is designed to equip learners with the mathematical foundations essential for understanding and developing AI systems. From linear algebra to optimization, we'll dive deep into the core concepts that power machine learning and neural networks.

Course Outline

  • Linear Algebra 📚
    Vectors, matrices, eigenvalues, and eigenvectors.

    linear_algebra

  • Probability & Statistics 📊
    Distributions, Bayes' theorem, and statistical inference.

    probability_statistics

  • Optimization Algorithms 🔍
    Gradient descent, convex optimization, and their applications in training models.

    optimization_algorithms

  • Calculus for Machine Learning 📈
    Derivatives, integrals, and their role in backpropagation.

    calculus_machine_learning

Why Take This Course?

  • Master the mathematical tools behind AI innovations
  • Solve real-world problems with data-driven models
  • Explore advanced topics like tensor calculus and stochastic processes
  • tensor_calculus

For more foundational math for AI, check out our Math Foundations for AI course! 📘

mathematics_ai