Now a day machine learning uses all the **mathematical concept** so it is necessary to have a strong mathematical concept.

In this blog, we will list all the important terms and concepts of mathematics which is related to machine learning.

**Start with probability ( Conditional Basic Marginal etc …)**

**Formula =>**

`P(Event) = Favourable Outcomes / Total Possible Outcomes . `

Let's look at some.

**Examples**:

**Problem: Throwing a Dice (1 time ) —** Means [1,2,3,4,5,6] ie. total possible outcomes = 6. What is the probability of getting 5 on throwing a dice ? Ans : 1 / 6 .

**Mathematical Series and Convergence, Numerical methods for Analysis**

Mostly it is defined using the limit.

**Examples**:

Imagine a sequence as such:

```
X0 = 1
X1 = 0.1
X2 = 0.01
X3 = 0.001
X4 =0.0001
...
Xn = 1/(10^n)
```

This means that Xn = 1/(10^5) converges to 0. As in "it can get closer and closer to zero" as much as we want.

**Bayesian Statistics**

Typically, one draws on Bayesian models for one or more of a variety of reasons, such as:

Having relatively few data points

Having strong prior intuitions

Having high levels of uncertainty

**Calculus**

Calculus is an important field in mathematics and it plays an integral role in many machine learning algorithms.

**Markov Process and Chains**

Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling

## Other topics:

### Stochastic Models

Here the list of all stochastic models:

**Poisson processes****Random Walk and Brownian motion processes****Gaussian Processes****etc.**

### Differential Equations

Differential Equations are very relevant for a number of machine learning methods.

### Dynamic Programming and Optimization Techniques

Dynamic programming works on the same lines as machine learning. It will explore each possibility and select the one which looks most probable at every step of the computation. Most of the reinforcement learning algorithms use dynamic programming.

**Examples** can be bots that need to decide for each step which action to take further when exploring.
Other being genetic algorithms, in-game theory, software agents or even algorithms for compressing and communicating data.

### Fourier's and Wavelengths

The Fourier transform (FT) decomposes a signal into the frequencies that make it up.

Mainly, the Fourier transform is represented as an indefinite integral.

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