Basics of Machine Learning

This power point presentation contains information like unsupervised learning, supervised learning and some topics in Manifolds.

1. Basics of Machine Learning

2. Contents Definition of Machine Learning Unsupervised & Supervised Learning Types of Unsupervised learning Manifolds LLE Algorithm

3. 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.

4. 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 – Black 1 – White

5. 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.

6. Supervised vs Unsupervised Learning In theoretical point of view both differ only in the casual structure of the model.

7. 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”.

8. Two types of Unsupervised Learning 1. Dimensionality Reduction 2. Density Estimation

9. 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)

10. 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

11. Advantages of Dimensionality Reduction Reduce Time complexity Reduce Space complexity More interpretable

12. 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.”

13. 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….

14. LLE Algorithm - Steps Step – 1: Compute the neighbors of each data point, Step – 2: Compute the weights Step – 3: Compute the vectors  

15. Conversion of High Dimension to Low Dimension

16. Thank you Presented by : Ch. Satya Pranav, KL University