Basics of Machine Learning

Basics of Machine Learning

Contents

Definition of Machine Learning

Unsupervised & Supervised Learning

Types of Unsupervised learning

Manifolds

LLE Algorithm

Definition of Machine learning

It is a branch of Artificial Intelligence, concerns the construction and study of systems that can learn from given data.

Dataset consists of data; data means it is a form of matrix.

In matrix rows are nothing but examples & columns are attributes of examples.

How pixels are stored as no’s in images ?

In images pixels will be used as no’s, if suppose an image is given of size 120*120, then the product will be 14,400 pixels.

Each pixel value will have 0 – 255 numbers.

If there are 25 images the matrix size is 25*14,400 pixels.

Pixels will be said based on intensity values 0 – Black1 – White

Gray scale

It is pronounced as ‘Grey Scale’.

These are also called ‘Monochromatic’

Grayscale is an image in which the value of each pixel is a single sample, that is it carries only intensity information.

Images of this sort, also known as black-and-white, are composed exclusively of shades of gray, varying from black at the Weakest intensity to white at strongest.

Supervised vs Unsupervised Learning

In theoretical point of view both differ only in the casual structure of the model.

Advantage of Unsupervised Learning

With unsupervised learning, it is possible to learn larger and more complex models than with supervised learning.

Unlabeled: This data might include photos, videos, audio recordings, etc. There is no explanation for each piece of unlabeled data – it just contains the data, and nothing else.

Labeled: This data typically takes a patch of unlabeled data & augments each piece of that unlabeled data with some sort of meaningful “tag”.

Two types of Unsupervised Learning

1. Dimensionality Reduction

2. Density Estimation

What is topology?

Topology is relationship between the points, “Location of point w.r.t another point around it.”

Topology means distances.

Example: Let us take points A,B,C

C ->>>>> 10 m ->>>>> A ->>>>> 5 m ->>>>> B (In High Dimension)

C ->>>>> 1 m ->>>>> A ->>>>> 0.5m ->>>>> B (In Low Dimension)

Dimensionality Reduction Types

1. Linear Method

(a) PCA – Principal Component Analysis

(b) MDS – Multi Dimensional Scaling

2. Non-Linear Method

(a) ISOMAP

(b) LLE – Locally Linear Embedding

Advantages of Dimensionality Reduction

Reduce Time complexity

Reduce Space complexity

More interpretable

Manifolds

“According to mathematics, it is a collection of points forming a certain kind of set, such as those of topologically closed surface.”

Example: Surface, Curve & point.

A Manifold has a dimension.

“A Manifold embedded in n-dimensional Euclidian space locally look like (n-1) dimensional vector space.”

LLE - Locally Linear Embedding

Main Aim of LLE is to convert high dimensional inputs to low dimensional outputs.

It is a Eigen vector method.

LLE is capable of generating highly non-linear embedding's.

In LLE, the transformation is non-linear.

In mathematics, linear in the sense no polynomials are involved in ‘X’.i.e. X^2, X^3 etc….

LLE Algorithm - Steps

Step – 1: Compute the neighbors of each data point, 𝑋𝑖

Step – 2: Compute the weights 𝑊𝑖𝑗

Step – 3: Compute the vectors 𝑌𝑖

Conversion of High Dimension to Low Dimension

Thank you

Presented by : Ch. Satya Pranav,

KL University