• Class and Course

    Applied mathematics fundamentals for AI

    In this comprehensive course, students will delve into the core mathematical concepts that are the building blocks of artificial intelligence (AI). Throughout the sessions, real-world examples and hands-on exercises will be provided, enabling students to apply these mathematical principles directly to AI challenges. By the conclusion of this training, students will have a deep understanding of the mathematical underpinnings of AI and be equipped with the quantitative skills necessary to develop their own algorithms and contribute to the field of AI with confidence.

    Linear Algebra:

    • Vectors and vector spaces
    • Matrices and matrix operations
    • Systems of linear equations
    • Eigenvalues and eigenvectors
    • Applications of linear algebra

    Probability and Information Theory:

    • Probability fundamentals
    • Basics of information theory
    • Common probability distributions
    • Hypothesis testing
    • Estimation techniques
    • Applications of probability and information theory


    • Introduction to Optimization
    • Linear Programming
    • Nonlinear Programming
    • Convex Optimization
    • Stochastic Optimization
    • Applications of Optimization

    Numerical Computation:

    • Introduction to Numerical Computation
    • Numerical Linear Algebra
    • Numerical Optimization
    • Interpolation and Approximation
    • Numerical Integration and Differentiation
    • Numerical Methods for Differential Equations

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