To browse Academia. Skip to main content. Log In Sign Up. Introduction to Machine Learning. Al Salam. Rationale: 1. For many problems, there is no known method for computing the desired output from a set of inputs. For other problems, computation according to the known correct method may be too expensive. It is very hard to write programs that solve problems like recognizing a face.
Instead of writing a program by hand, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. It may contain millions of numbers.
The web contains a lot of data. Million dollar prize! Some tasks cannot be defined well, except by examples e.
Free Machine Learning PowerPoint Template
Relationships and correlations can be hidden within large amounts of data. Human designers often produce machines that do not work as well as desired in the environments in which they are used. The amount of knowledge available about certain tasks might be too large for explicit encoding by humans e.
Environments change over time. New knowledge about tasks is constantly being discovered by humans. Areas of Influence for Machine Learning 1. Statistics: How best to use samples drawn from unknown probability distributions to help decide from which distribution some new sample is drawn? Brain Models: Non-linear elements with weighted inputs Artificial Neural Networks have been suggested as simple models of biological neurons. Adaptive Control Theory: How to deal with controlling a process having unknown parameters that must be estimated during operation?
Psychology: How to model human performance on various learning tasks?The Machine learning Template in PowerPoint format includes two slides. Firstly there are types of the Statistical machine learning. Secondly supervised learning process is the most important one of the Statistical machine learning. So our PowerPoint templates are including supervised learning, unsupervised learning, and Reinforcement learning. Then Supervised learning process diagram shows the detailed steps.
It is evolving from the study of pattern recognition and computational learning theory in artificial intelligence. Then the learning explores the study and construction of algorithms that can learn from and make predictions on data. Finally the learning tasks are typically classified into three broad categories; supervised learning, unsupervised learning, and Reinforcement learning.
You can found them in our Statistical machine learning PowerPoint templates. We know that supervised learning is the learning task of inferring a function from labeled training data.
Therefore the aim of supervised machine-learning is to build a model that makes predictions based on train data-set. So after data cleaning and feature extraction, data-set is classifying into training data-set and testing one for prediction.
Meanwhile algorithms are applying to the training data-set and the training models are developed and selected. Then the selected models will run with the testing data to cross check the accuracy of prediction.
Finally the models will be evaluated and modified to improve the prediction. The labels and shapes are layers of different groups. In a word, you can change the texts, colors, sizes, and add your charts. At the same time you can also refer to the detailed Machine learning in Wikipedia.
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Toggle navigation. Help Preferences Sign up Log in. To view this presentation, you'll need to allow Flash. Click to allow Flash After you enable Flash, refresh this page and the presentation should play. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Title: Machine Learning. Tags: applications fuzzy learning machine. Latest Highest Rated. Title: Machine Learning 1 Machine Learning Artificial Intelligence Department of Industrial Engineering and Management Cheng Shiu University 2 Outline Artificial intelligence in 21st century Learning Machine learning Supervised learning How brain works Neural network and Artificial neural networks Simple neuron - Perceptron 3 Artificial Intelligence The capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system, a program for CAD or CAM, or a program for the perception and recognition of shapes in computer vision systems 4 Business Intelligence The process of gathering information about a business or industry matter a broad range of applications and technologies for gathering, storing, analyzing, and providing access to data to help make business decisions BI 5 Computational Intelligence An offshoot of artificial intelligence.
Several expressions compete to name the same interdisciplinary area. It is difficult, if not impossible, to accommodate in a formal definition disparate areas with their own established individualities such as fuzzy sets, neural networks, evolutionary computation, machine learning, Bayesian reasoning, etc.
Bringing together diverse expertise and experience can enrich each of the participating discipline and foster new research perspectives in the broad field of Computational Intelligence. Zadeh " The ease with which they can learn led to attempts to emulate a biological neural network in a computer. Learning capabilities can improve the performance of an intelligent system over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms.
The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today. Each neuron has a very simple structure, but an army of such elements constitutes a tremendous processing power. A neuron consists of a cell body, soma, a number of fibers called dendrites, and a single long fiber called the axon.
Information is stored and processed in a neural network simultaneously throughout the whole network, rather than at specific locations. In other words, in neural networks, both data and its processing are global rather than local.
The neurons are connected by weighted links passing signals from one neuron to another. The output signal is transmitted through the neurons outgoing connection. The outgoing connection splits into a number of branches that transmit the same signal.
The outgoing branches terminate at the incoming connections of other neurons in the network. If the net input is less than the threshold, the neuron output is 1. But if the net input is greater than or equal to the threshold, the neuron becomes activated and its output attains a value 1.
The neuron uses the following transfer or activation function This type of activation function is called a sign function. The perceptron is the simplest form of a neural network.
It consists of a single neuron with adjustable synaptic weights and a hard limiter.Electronics and digital devices have made the world full of artificial utensils. Now the world is full of artificial products relating to almost all fields of life. The template design is suitable for electronic devices using motherboard and chips. The electronic or artificial products business can also use this PPT template design for their discussions and publicity.
The slide design has neurotransmitters and a brain shape design that is also suitable for other presentation purposes, including IQ, Telemedicine, Neuroscience, or other intelligence related topics. You can download the template design freely and make wonderful PPT presentations by using it in the main slide or as the background of the presentation.
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Machine Learning - PowerPoint PPT Presentation
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