Important Statistics, Calculus, and Algebraic Equations in AI/ML

Artificial Intelligence (AI) and Machine Learning (ML) heavily rely on a wide range of statistical, calculus, and algebraic concepts. Below is an overview of some key equations and concepts used in these fields:

Statistics

1. Probability Distributions:

2. Bayes’ Theorem:

3. Expectation and Variance:

4. Covariance and Correlation:

5. Hypothesis Testing:

Calculus

1. Derivatives and Gradients:

2. Integrals:

3. Optimization:

4. Differential Equations:

Linear Algebra

1. Vectors and Matrices:

2. Eigenvalues and Eigenvectors:

3. Singular Value Decomposition (SVD):

4. Principal Component Analysis (PCA):

Machine Learning Specific Equations

1. Linear Regression:

2. Logistic Regression:

3. Neural Networks:

4. Support Vector Machines (SVM):

5. K-Means Clustering:

These equations and concepts form the backbone of many algorithms in AI and ML, providing the mathematical foundation needed for model development, training, and evaluation.