Basics of Machine Learning
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Transcript of Basics of Machine Learning
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Basics of Machine Learning
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Contents
Definition of Machine Learning
Unsupervised & Supervised Learning
Types of Unsupervised learning
Manifolds
LLE Algorithm
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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.
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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
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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.
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Supervised vs Unsupervised Learning
In theoretical point of view both differ only in the casual structure of the model.
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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”.
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Two types of Unsupervised Learning
1. Dimensionality Reduction
2. Density Estimation
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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)
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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
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Advantages of Dimensionality Reduction
Reduce Time complexity
Reduce Space complexity
More interpretable
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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.”
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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….
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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
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Conversion of High Dimension to Low Dimension
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Thank you
Presented by : Ch. Satya Pranav,
KL University