Machine learning introduction

Machine learning introduction

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  Athira l
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   It isan area of computer science that emphasizes the creation of intelligent machines that work and react like humans  The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”.  Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think.  There is a misconception that Artificial Intelligence is a system, but it is not a system .  AI is implemented in the system.  “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is a intelligence where we want to add all the capabilities to machine that human contain.
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   The abilityto learn without being explicitly programmed  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.  The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.  The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
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   Machine Learningis the learning in which machine can learn by its own without being explicitly programmed.  It is an application of AI that provide system the ability to automatically learn and improve from experience.  Here we can generate a program by integrating input and output of that program.  One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
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  ARTIFICIAL INTELLIGENCE MACHINELEARNING AI stands for Artificial intelligence, where intelligence is defined acquisition of knowledge intelligence is defined as a ability to acquire and apply knowledge. ML stands for Machine Learning which is defined as the acquisition of knowledge or skill The aim is to increase chance of success and not accuracy. The aim is to increase accuracy, but it does not care about success It work as a computer program that does smart work It is a simple concept machine takes data and learn from data. The goal is to simulate natural intelligence to solve complex problem The goal is to learn from data on certain task to maximize the performance of machine on this task.
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   Deep learningis actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.  The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance.  If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.  Let’s take an example to understand both machine learning and deep learning – Suppose we have a flashlight and we teach a machine learning model that whenever someone says “dark” the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word “dark” and as the word comes the flashlight will be on but what if someone said “I am not able to see anything the light is very dim”, here the user wants the flashlight to be on but the sentence does not the consist the word “dark” so the flashlight will not be on. That’s where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.
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   Machine learningand deep learning is a way of achieving AI, which means by the use of machine learning and deep learning we may able to achieve AI in future but it is not AI.
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   Supervised machinelearning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
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   In contrast,unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
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   Semi-supervised machinelearning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringunlabeled data generally doesn’t require additional resources.
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   Reinforcement machinelearning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
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   Machine learningenables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
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   A humaneye has between six and seven million cone cells, containing one of three colour-sensitive proteins known as opsins. When photons of light hit these opsins, they change shape, triggering a cascade that produces electrical signals, which in turn transmit the messages to the brain for interpretation.  This whole process is a very complex phenomenon and making a machine to interpret this at a human level has always been a challenge. The motivation behind the modern-day machine vision system lies at the core of emulating human vision for recognising patterns, faces and rendering 2D imagery from a 3D woThere is a lot of overlap between image processing and computer vision at the conceptual level and the jargon, often misunderstood, is being used interchangeably. Here we give a brief overview of the techniques and explain how they are different at the fundamental level.rld into 3D.
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   Digital imageprocessing was pioneered at NASA’s Jet Propulsion Laboratory in the late 1960s, to convert analogue signals from the Ranger spacecraft to digital images with computer enhancement. Now, digital imaging has a wide range of applications, with particular emphasis on medicine. Well-known uses for it include Computed Aided Tomography (CAT) scanning and ultrasounds.
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   Image Processingis mostly related to the usage and application of mathematical functions and transformations over images regardless of any intelligent inference being done over the image itself. It simply means that an algorithm does some transformations on the image such as smoothing, sharpening, contrasting, stretching on the image.  For a computer, an image is a two-dimensional signal, made up of rows and columns of pixels. An input of one form can sometimes be transformed into another. For instance, Magnetic Resonance Imaging (MRI), records the excitation of ions and transforms it into a visual image.
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   Here’s anexample of smoothing images with Python:  As for one-dimensional signals, images also can be filtered with various low-pass filters (LPF), high-pass filters (HPF), etc. An LPF helps in removing noise or blurring the image. An HPF filter helps in finding edges in an image.  Via OpenCV documentationThese type of transformations using matrices are quite prevalent in machine learning algorithms like convolution neural network. Where a filter is convolved over an image(another matrix of pixel values) to detect edges or colour intensities.  Some techniques which are used in digital image processing include: ◦ Hidden Markov models ◦ Image editing and restoration ◦ Linear filtering and Bilateral filtering  Neural networks
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   Computer visioncomes from modelling image processing using the techniques of machine learning. Computer vision applies machine learning to recognise patterns for interpretation of images. Much like the process of visual reasoning of human vision; we can distinguish between objects, classify them, sort them according to their size, and so forth. Computer vision, like image processing, takes images as input and gives output in the form of information on size, colour intensity etc.
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   Below arethe components of a standard machine vision system: ◦ Camera ◦ Lighting devices ◦ Lens ◦ Frame grabber ◦ Image processing software ◦ Machine learning algorithms for pattern recognition  Display screen or a robotic arm to carry out an instruction obtained from image interpretation.  For instance, a video camera mounted on a driverless car has to detect people in front of it and distinguish them from vehicles and other distinctive features. Or, we may want to measure the distance covered by a tennis player in a game.
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   Therefore, temporalinformation plays a major role in computer vision, much like it is with our own way of understanding the world.  The ultimate goal here is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs.
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   Image processingis a subset of computer vision. A computer vision system uses the image processing algorithms to try and perform emulation of vision at human scale. For example, if the goal is to enhance the image for later use, then this may be called image processing. And if the goal is to recognise objects, defect for automatic driving, then it can be called computer vision.