1.1 Machine Learning
The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Machine learning is closely related not only to data mining and statistics, but also theoretical computer science. Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, brain-machine interfaces and cheminformatics, detecting credit card fraud, stock market analysis, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion [1].
Machine learning is usually divided to two types, supervised learning and unsuper- vised learning.
1.2 Types
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A toy example of classification, you would like to use software to examine individual customer accounts, and for each account we need to decide if it has been hacked or compromised. We have two classes of objects which cor- respond to hacked or compromised. The input are individual customer accounts. These have been described by a set of D features or attributes, which are stored in an N * M matrix X. Also we have a training vector Y (hacked = 1; compro- mised = 0). Thus we are required to generalize beyond the training set and find which attributes belong to hacked and which attributes belong to compromised. There exist a number of supervised learning classification algorithms, for example, Decision Tree and Naive Bayes classification …show more content…
The goal is to discover internal connection in these data. Unlike supervised learning, these data cannot told what the desired outputs for each input. Unsupervised learning is more typical of human learning. It is more widely used than supervised learning, since it does not require a human experience (no need to labeled data). La belled data is not only expensive, but also cannot provide us with enough information. The example of unsupervised learning is clustering data into groups. X1 and X1 denotes the attributes Of input data, but they has not given outputs. It seems that there might be two clusters, or subgroups. Our goal is to estimate which cluster each point belongs to. There are three basic clustering methods: the classic K-means algorithm, incremental clustering, and the probability based clustering method. The classic k-means algorithm forms clusters in numeric domains, partitioning instances into disjoint clusters, while incremental clustering generates a hierarchical grouping of
Artificial Intelligence (AI) is one of the newest fields in Science and Engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956 by John McCarthy. Artificial Intelligence is an art of creating machines that perform functions that require intelligence when performed by people [Kurzweil, 1990]. It encompasses a huge variety of subfields, ranging from general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on the crowded street, and diagnosing diseases. Artificial Intelligence is relevant to any intellectual task; it is truly a Universal field. In future, intelligent machines will replace or enhance human’s capabilities in
"My name is Dorothy," said the girl, "and I am going to the Emerald City, to ask the Oz to send me back to Kansas."
We live in a world that can’t live without binary code anymore. Computers have pervaded so deep in our lives that they are now being called ubiquitous. With phenomenal increase in users, has come a phenomenal increase in data. We generate a vast amount of data through activities on our computing devices making it necessary to employ intelligent algorithms which enable the system to learn from and analyze this vast dataset. Fortunately, the advent of Distributed Computing has created avenues to access virtually limitless computing power even through mobile devices thus, allowing us to use highly complex and large scale algorithms. However, with all this power under the hood, it is important to make the computers as usable and receptive to users as possible. I believe this interdisciplinary paradigm will have far reaching impact on industries, governments as well as our daily lives which is why I am so interested in research concerning Information Management and Analytics, Artificial Intelligence, Human Computer Interaction, and Mobile and Internet Computing.
From a technical perspective, machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experiences without being explicitly programmed (N.A 2017). Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves (N.A 2017). Unlike human learning, machine learning is based on algorithms.
c. Machine Learning: Uses data to train the algorithm and proves effective when we have little knowledge of what we are looking for.
Scientists claim that devices with Artificial Intelligence will replace office workers during next 5 years (Maksimova).According to this statement it is possible to say that AI has a great influence on humanity. Pursuant to Oxford Dictionary Artificial Intelligence or AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages(dictionary).Firstly, this research will analyze positive and negative impacts of development of Artificial Intelligence on economic sphere. Then, author going to discuss social effects of Artificial Intelligence. After the considering all perspectives that link to this topic, the last step will be to draw a conclusion.
Data is collected and the patterns are recognized, in order to understand the physical properties, and further to visualize the data as
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Selecting, in which the learner attends to relevant aspects of the incoming visual and auditory information ( as indicated by the arrows from sensory memory to working memory)
It could be argued that machine learning is influencing the way we perceive information and think. From customer service software to Google search algorithms, machine learning is already becoming a daily phenomenon that is aiding us towards making better and faster decisions. Machine learning is best defined as an artificial intelligence (AI) approach in which machines are allowed to learn and make further decisions about certain outcomes without programming it to. In this paper, I will further define what machine learning is and by using Facebook’s Messenger Platform as an example, I will showcase how machine learning can be implemented in our everyday life.
Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize certain patterns and situations and apply these to certain priorities and output appropriate results. These types of neural networks can be used in many important situations such as priority in an emergency room, for financial assistance, and any type of pattern recognition such as handwritten or text-to-speech recognition.
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Some machine learning works in a way similar to the way people do it. Google Translate, for example, uses a large database of text in a given language to translate to another language, a statistical process that doesn 't involve looking for the "meaning" of words. Humans, do something similar, in that we learn languages by seeing lots of examples. Google Translate doesn 't always get it right, precisely because it doesn 't seek meaning and can sometimes be fooled by synonyms or differing connotations. (Schapire, 2008) Current and future examples of machine learning include; optical character recognition, face detection, spam filtering, fraud detection, weather prediction and medical
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?