# AI and Algebra

**- Overview**

Algebra is one of the broad areas of mathematics, together with number theory, geometry and analysis. In its most general form, algebra is the study of mathematical symbols and the rules for manipulating these symbols; it is a unifying thread of almost all of mathematics. It includes everything from elementary equation solving to the study of abstractions such as groups, rings, and fields.

Algebra is divided into different sub-branches such as elementary algebra, advanced algebra, abstract algebra, linear algebra, and commutative algebra. The more basic parts of algebra are called elementary algebra; the more abstract parts are called abstract algebra or modern algebra.

Elementary algebra is generally considered to be essential for any study of mathematics, science, or engineering, as well as such applications as medicine and economics. Abstract algebra is a major area in advanced mathematics, studied primarily by professional mathematicians.

Please refer to the following for details:

- Wikipedia:
**Algebra** **Algebra Formula Sheet****UC-Davis: Linear Algebra**- Wikipedia:
**Linear Algebra**

**- Elementary Algebra**

Elementary algebra is a branch of mathematics that covers the basics of algebra. In general, knowledge of algebra may be the foundation of mathematics.

Elementary algebra is different from arithmetic, which deals with specified numbers. Algebra introduces variables, which are quantities without fixed values.

Elementary algebra covers:

- Real number properties
- Variables
- Graphing in one and two dimensions
- Basic addition, subtraction, multiplication, and division
- Linear and quadratic equations
- Polynomials
- Exponents
- Rational expressions

**- Algebraic Equation**

Algebra helps to solve mathematical equations and allows to derive unknown quantities such as bank interest, ratios, percentages. We can use variables in algebra to represent unknowns coupled in a way that rewrites the equation.

Algebra is constructive in stating mathematical equations and relationships by using letters or other symbols that represent entities. The unknowns in the equations can be solved algebraically.

In mathematics, an algebraic equation is an equation of the form, where P is a polynomial with coefficients in some field.

There are three types of equations based on the degree: linear equation, quadratic equation, and cubic equation.

Algebraic equations are always balanced equations that include variables, coefficients, and constants. Here are some algebraic equations:

- Quadratic formula: Used to solve quadratic equations, the general form is ax2 + bx + c = 0, where x is the variable, a is the coefficient, and c is the constant.
- Discriminant: For a quadratic equation ax2 + bx + c = 0 where a ≠ 0, the discriminant is Δ = b2 − 4ac.
- Monomial: A polynomial with a single term, but can have multiple variables and a higher degree. For example, 9x3yz is a single term, where 9 is the coefficient, x, y, z are the variables and 3 is the degree of monomial.

**- Modern Algebra**

In mathematics, more specifically, algebra, abstract algebra or modern algebra is the study of algebraic structures. Algebraic structures include algebras on groups, rings, fields, modules, vector spaces, lattices, and fields.

The term abstract algebra was coined in the early 20th century to distinguish this field of study from earlier parts of algebra and, more specifically, from elementary algebra (the use of variables to represent numbers in computation and reasoning).

Algebraic structures and their associated homomorphisms constitute mathematical categories. Category theory is a formalism that allows a variety of structurally similar properties and structures to be expressed in a unified way.

Universal algebra is a related discipline that studies the types of algebraic structures as a single object. For example, the structure of a group is a single object in universal algebra called the diversity of the group.

**- Linear Algebra**

Artificial Intelligence is not a single subject it has sub-fields like Learning (Machine Learning & Deep Learning), Communication using NLP, Knowledge Representation & Reasoning, Problem Solving, Uncertain Knowledge & Reasoning.

Linear algebra is the foundation of machine learning, from the notations used to describe the algorithms, to the implementation of the algorithms itself.

Linear algebra is a branch of algebra that applies to both applied and pure mathematics. It handles linear mappings between vector spaces. It also involves the study of planes and lines. It is the study of systems of linear equations with transformation properties. It is used in almost all areas of mathematics. It deals with linear equations of linear functions and their representation in vector spaces and matrices.

**- Linear Algebra for AI and Quantum Computing**

Linear algebra is a key tool for AI and quantum computing (QC). It's used in fundamental AI tasks like data analysis, image recognition, and signal processing.

Linear algebra is a branch of applied mathematics that's essential for AI experts. It's used in AI chatbots for tasks like word embeddings, which convert words into numerical vectors for analysis and understanding.

Here are some other ways linear algebra is used in AI:

- Linear transformations: Linear algebra is used in linear transformations, which are fundamental operations for AI tasks.
- Matrix representations: Matrix representations allow AI systems to perform meaningful operations and examine data through linear transformations.
- Data representation and transformation: Matrices and vectors, which are core concepts in linear algebra, are used for data representation and transformation.

**[More to come ...]**