Basics of Machine Learning
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This document provides an overview of machine learning concepts including supervised and unsupervised learning. It defines machine learning as a branch of artificial intelligence that uses data to learn. Unsupervised learning can learn more complex models than supervised learning from unlabeled data without explanations. Dimensionality reduction and density estimation are two types of unsupervised learning. Locally linear embedding (LLE) is a nonlinear dimensionality reduction technique that converts high-dimensional data into a lower-dimensional representation while preserving local neighborhoods. The LLE algorithm involves computing neighbors of each data point, weights between points, and vectors to perform the dimensionality reduction.
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Basics of Machine Learning
- 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, 푋푖 Step – 2: Compute the weights 푊푖푗 Step – 3: Compute the vectors 푌푖
- 15. Conversion of High Dimension to Low Dimension
- 16. Thank you Presented by : Ch. Satya Pranav, KL University
Editor's Notes
- Machine learning & data mining are commonly related, as they often employ same method & overlap significantly. Machine learning: It focuses on prediction, based on known properties learned from the training data. Data mining : It focuses on the discovery of unknown properties in data.
- Example: If we want to classify different animals, first they will provide data in matrix. Rows are lion, tiger, cat. Columns are Height, weight, shape etc.
- Note: In Supervised learning categories are known, but in Unsupervised learning categories are not known. In Supervised learning, one set of observations, called ‘inputs’ is assumed to be the cause of another set of observations called ‘outputs’. In Supervised learning your examples are labeled, but in Unsupervised learning your examples are not labeled.
- Example: An hypothetical non-machine learning algorithm for face recognition in images would try to define what a face is. A machine learning algorithm would not have such coded definition but will “learn-by-example”, you’ll show several images of faces and non-faces and a good algorithm will eventually learn and be able to predict whether/not an unseen image is a face. This particular example of face recognition is supervised, which means that your examples must be labeled. Explicitly says which one are faces and which one aren’t. Note: Augments = Make greater / Increase.
- “Dimensionality Reduction is also called as Dimension reduction, which is used to reduce the total number of random variables under consideration. Means we have to reduce the Size/Dimension of the given image with out disturbing the topology.
- Space Complexity: In general datasets there are very less attributes which classify the data. That’s an experimental observation, for example the bank dataset each row has 10,000 column (say) but the most important are just 10. Not all are equally helpful in classification. Some are helpful, so dimensionality reduction find the most important columns. It may not exactly be the 5th column, 10th column or 100th column, but sometimes it just gives completely different vector like a 10-D vector for a 10,000-D vector. NOTE: A matrix with 10,000 columns to 10 columns is a visible reduction in space.
- A Surface is a 3-D object but 2-D manifold. A curve is a 2-D object but 1-D manifold. A point is a 1-D object bur 0-D manifold Example: Earth (A big sphere ) is a big manifold embedded in 3-dimensional space, but we as tiny entities living on its surface can only see flat 2-dimensional land. So, locally at every point on a sphere, It looks like a 2-D plane.