Computer Science
Machine Learning Models
Machine learning models are algorithms that can learn from and make predictions or decisions based on data. These models are trained using large datasets and can be used for tasks such as classification, regression, clustering, and recommendation. They are a key component of artificial intelligence and are used in various applications such as image and speech recognition, medical diagnosis, and financial forecasting.
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11 Key excerpts on "Machine Learning Models"
eBook - PDF- Michael Paluszek, Stephanie Thomas(Authors)
- 2016(Publication Date)
- Apress(Publisher)
PART I Introduction to Machine Learning CHAPTER 1 An Overview of Machine Learning 1.1 Introduction Machine learning is a field in computer science where existing data are used to predict, or respond to, future data. It is closely related to the fields of pattern recognition, computational statistics, and artificial intelligence. Machine learning is important in areas like facial recognition, spam filtering, and others where it is not feasible, or even possible, to write algorithms to perform a task. For example, early attempts at spam filtering had the user write rules to determine what was spam. Your success depended on your ability to correctly identify the attributes of the message that would categorize an email as spam, such as a sender address or subject keyword, and the time you were willing to spend to tweak your rules. This was only moderately successful as spam generators had little difficulty anticipating people’s rules. Modern systems use machine learning techniques with much greater success. Most of us are now familiar with the concept of simply marking a given message as “spam” or “not spam,” and we take for granted that the email system can quickly learn which features of these emails identify them as spam and prevent them from appearing in our inbox. This could now be any combination of IP or email addresses and keywords in the subject or body of the email, with a variety of matching criteria. Note how the machine learning in this example is data-driven, autonomous, and continuously updating itself as you receive email and flag it. In a more general sense, what does machine learning mean? Machine learning can mean using ma-chines (computers and software) to gain meaning from data. It can also mean giving machines the ability to learn from their environment. Machines have been used to assist humans for thousands of years. Con-sider a simple lever, which can be fashioned using a rock and a length of wood, or the inclined plane.
eBook - ePubArtificial Intelligence for Business Optimization
Research and Applications
- Bhuvan Unhelkar, Tad Gonsalves(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
Chapter 4 Machine learning types Statistical understanding in the business contextMachine Learning overview
Traditionally, solving problems using a computer involves writing detailed instructions in the form of a code. Machine learning (ML) extends this problem-solving ability of computers without being explicitly programmed. Alan Turing, in a talk given to the London Mathematical Society in 1947,1 predicted ML, saying “what we want is a machine that can learn from experience.” Later, in 1959, Arthur Samuel defined ML as “the field of study that gives computers the ability to learn without being explicitly programmed.”2 Tom Michel gave a formal definition of ML as, “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”3ML is of interest to business because of its ability to solve business problems. This discussion on BO is a business issue that aims to use ML to enable it to provide customer value.Traditionally, computers do exactly what they are told to do. Algorithm refers to a detailed set of unambiguous steps given to the computer to solve a problem. These steps are coded in the form of a computer program, loaded in the RAM, and executed. After execution, the results are presented as visuals. Business people use these visuals to make decisions.Applying ML
Most problems in science, engineering, economics, and finance are solved by means of equations. An equation is generally in a parametric form, where the parameters (or variables) are related to one another. Plugging in the values of known variables and performing well-defined mathematical operations on the equation yield the values of unknown variables. The parametric equations are in a functional form, where the unknown variable is expressed as a function of the known variables. The problem-solving strength of the above disciplines rests on the functions which relate the unknown variable to the known variables. Difficulties arise in solving problems which have no function connecting the dependent and independent variables. ML handles these types of problems.
- Zoran Gacovski(Author)
- 2019(Publication Date)
- Arcler Press(Publisher)
Machine Learning aims to generate classifying expressions simple enough to be understood easily by the human. They must mimic human reasoning sufficiently to provide insight into the decision process. Like statistical approaches, background knowledge may be exploited in development, but operation is assumed without human intervention. To learn is: • to gain knowledge, comprehension, or mastery of through experience or study or to gain knowledge (of something) or acquire skill in (some art or practice) • to acquire experience of or an ability or a skill in • to memorize (something), to gain by experience, example, or practice. Machine Learning can be defines as a process of building computer systems that automatically improve with experience, and implement a learning process. Machine Learning can still be defined as learning the theory automatically from the data, through a process of inference, model fitting, or learning from examples: • Automated extraction of useful information from a body of data by building good probabilistic models. • Ideally suited for areas with lots of data in the absence of a general theory. A major focus of machine learning research is to automatically produce models and a model is a pattern, plan, representation, or description designed to show the main working of a system, or concept, such as rules determinate rule for performing a mathematical operation and obtaining a certain result, a function from sets of formulae to formulae, and patterns ( model which can be used to generate things or parts of a thing from data. Learning is a MANY-FACETED PHENOMENON as described by Jaime et al (Jaime G. Carbonell, 1983) and also stated that Learning processes include the acquisition of new declarative knowledge, the development of motor and cognitive skills through instruction or practice, the organization of new knowledge into general, effective representations, and the discovery of new facts and theories through observation and experimentation.
eBook - PDFDeep Learning for Targeted Treatments
Transformation in Healthcare
- Rishabha Malviya, Gheorghita Ghinea, Balamurugan Balusamy, Sonali Sundram, Rajesh Kumar Dhanaraj(Authors)
- 2022(Publication Date)
- Wiley-Scrivener(Publisher)
Due to their failure to manage categorical data, deal with missing data points, spread of data points, and, most crucially, lack of reasoning abilities, the number of researches utilizing non-traditional methodologies such as machine learning is assisting much in this regard [2]. It’s a sub-discipline of artificial intelligence that allows machines that can learn from their mistakes and examples in the same way that people do, and to discover fascinating patterns without having to be pro- grammed. The algorithm is fed data, which is then used to create a model. It can forecast new values using this model. It assists us in locating something unfamiliar to us, which may lead to the discovery of many new things [3]. By definition, machine learning is considered to be the subset of com- puter science that arose from artificial intelligence research into pattern rec- ognition and computational learning theory. Machine learning systems may undertake difficult tasks instead of just pre-programming by learning from data by enabling computers to accomplish certain jobs intelligently [4]. Machine learning has made significant advancements in the last few years, expanding its capabilities across a wide range of applications. Machine learning algorithms can now be trained on a vast pool of instances thanks to increased data availability, and their analytical skills have been bolstered by increased computer processing power. There have been sta- tistical advancements within the sector, giving machine learning more strength. As a result of these advancements, computers that performed considerably below human levels only a few years ago can now surpass humans at some tasks [5]. Many individuals today engage with machine learning-based systems on a periodic basis, such as social network photo identification schemes, Machine Learning as a Scientific Discipline 409 speech processing platforms used by virtual agents, and online store sys- tems for advocacy.
eBook - PDF- David Barber(Author)
- 2012(Publication Date)
- Cambridge University Press(Publisher)
Part III Machine learning Machine learning is fundamentally about extracting value from large datasets. Often the motivation is ultimately to produce an algorithm that can either mimic or enhance human/biological performance. In Part III we begin by discussing some of the basic concepts in machine learning, namely supervised and unsupervised learning. We then move on to discuss some standard models in machine learning and related areas. 13 Machine learning concepts Machine learning is the body of research related to automated large-scale data analysis. Historically, the field was centred around biologically inspired models and the long-term goals of much of the community are oriented to producing models and algorithms that can process information as well as biological systems. The field also encompasses many of the traditional areas of statistics with, however, a strong focus on mathematical models and also prediction. Machine learning is now central to many areas of interest in computer science and related large-scale information processing domains. 13.1 Styles of learning • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Broadly speaking the main two subfields of machine learning are supervised learning and unsuper-vised learning . In supervised learning the focus is on accurate prediction, whereas in unsupervised learning the aim is to find compact descriptions of the data. In both cases, one is interested in methods that generalise well to previously unseen data. In this sense, one distinguishes between data that is used to train a model and data that is used to test the performance of the trained model, see Fig.
- Botsch, Michael(Authors)
- 2023(Publication Date)
- Cuvillier Verlag(Publisher)
2. Machine Learning The branch of science that deals with the automatic discovery of regularities in data through the use of computer algorithms is called machine learning. If the discovery of regularities in data is not necessarily coupled to the use of computers one talks about statistical learning. Machine learning plays an important role in the areas of data mining, artificial intelligence, statistics and in various engineering disciplines. The focus of this thesis lies on the latter, aiming to use machine learning for the design of technical systems that have to react to signals coming from the environment by tuning the parameters of an adaptive model in such a way that an application-specific behavior is realized. The first section of this chapter introduces the basics underlying statistical learning. Sec- tion 2.2 presents state of the art algorithms for machine learning with a focus on linear basis expansion models, Classification and Regression Trees (CART), and the Random For- est (RF) algorithm since these methods are the basis for techniques that are developed later in the thesis for the task of temporal classification. Section 2.3 addresses the problem of finding the most compact and informative representation of data which is then used by a machine learning algorithm to realize the desired behavior. 2.1 Basics of Statistical Learning Many relations that are found by statistical learning methods in data can be represented in the form of classification or regression functions. Classification and regression aim at estimating values of an attribute of a system based on previously measured attributes of this system. Given a set of measured observation attributes v = [v 1 , . . . , v N ] T ∈ R N , statistical learning methods estimate the values of a different attribute y. If y takes on continuous numerical values, i. e., y ∈ R one talks about regression and if it takes on discrete values from a set of K categorical values, called classes, i.
- Budati Anil Kumar, S. B. Goyal, Sardar M. N. Islam(Authors)
- 2022(Publication Date)
- Wiley-Scrivener(Publisher)
Understanding the process needs materials, intricate design, and process interactions throughout a complicated multi-stage develop- ment process having five vital steps. For developing an acute quality part, these processes need to be handled precisely for accurate execution. Machine Learning for AM Developing and studying systems that can learn the patterns from the data automatically is the main aim of machine learning techniques. Tasks such as performance optimization, defeat detection, forecasting, regression, classification, and prediction can be efficiently done by models that are formulated by ML [2]. The effectiveness of the ML technique is determined by the data applied for training the Machine Learning model. The efficacy of the trained data is directly proportional to the efficacy of ML models. There are two categories of ML techniques: supervised learning and unsupervised learning [3]. A training data with a labeled set offers a sample of the input values and the related true output value is present in the super- vised learning. The model data trained by the ML algorithm gives an idea about the functional relationship between the output and input areas. The process of classification and regression is used by supervised learning [4]. Learning Algorithms and Models in 3D Printing 49 Labeled training data sets are not available in unsupervised learning. Divergent conditions can be detected by the applications of unsupervised learning [5]. The benefits from the given scenario decide the utilization of the supervised or unsupervised ML approach. Different ML algorithms can be further classified by the high-level clas- sification that is provided by supervised and unsupervised models. Support Vector Machines (SVM), as well as Neural Networks (NN), are two ML models that are used for classification and regression [6]. Identifying a hyperplane that segments the data into various classes is facilitated by the SVM model.
eBook - PDF- Yagang Zhang(Author)
- 2010(Publication Date)
- IntechOpen(Publisher)
Types of Machine Learning Algorithms 19 X Types of Machine Learning Algorithms Taiwo Oladipupo Ayodele University of Portsmouth United Kingdom 1. Machine Learning: Algorithms Types Machine learning algorithms are organized into taxonomy, based on the desired outcome of the algorithm. Common algorithm types include: • Supervised learning --- where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --- which models a set of inputs: labeled examples are not available. • Semi-supervised learning --- which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --- similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --- where the algorithm learns its own inductive bias based on previous experience. The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory. Machine learning is about designing algorithms that allow a computer to learn. Learning is not necessarily involves consciousness but learning is a matter of finding statistical regularities or other patterns in the data. Thus, many machine learning algorithms will barely resemble how human might approach a learning task.
eBook - PDFFundamentals and Methods of Machine and Deep Learning
Algorithms, Tools, and Applications
- Pradeep Singh(Author)
- 2022(Publication Date)
- Wiley-Scrivener(Publisher)
1 Pradeep Singh (ed.) Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, (1–16) © 2022 Scrivener Publishing LLC 1 Supervised Machine Learning: Algorithms and Applications Shruthi H. Shetty*, Sumiksha Shetty † , Chandra Singh ‡ and Ashwath Rao § Department of ECE, Sahyadri College of Engineering & Management, Adyar, India Abstract The fundamental goal of machine learning (ML) is to inculcate computers to use data or former practice to resolve a specified problem. Artificial intelligence has given us incredible web search, self-driving vehicles, practical speech affirma- tion, and a massively better cognizance of human genetic data. An exact range of effective programs of ML already exist, which comprises classifiers to swot e-mail messages to study that allows distinguishing between unsolicited mail and non-spam messages. ML can be implemented as class analysis over super- vised, unsupervised, and reinforcement learning. Supervised ML (SML) is the subordinate branch of ML and habitually counts on a domain skilled expert who “teaches” the learning scheme with required supervision. It also generates a task that maps inputs to chosen outputs. SML is genuinely normal in characteriza- tion issues since the aim is to get the computer, familiar with created descrip- tive framework. The data annotation is termed as a training set and the testing set as unannotated data. When annotations are discrete in the value, they are called class labels and continuous numerical annotations as continuous target values. The objective of SML is to form a compact prototype of the distribution of class labels in terms of predictor types. The resultant classifier is then used to designate class labels to the testing sets where the estimations of the predictor types are known, yet the values of the class labels are unidentified. Under certain assumptions, the larger the size of the training set, the better the expectations on the test set.
eBook - PDF- Rex Porbasas Flejoles(Author)
- 2019(Publication Date)
- Arcler Press(Publisher)
TYPES OF MACHINE LEARNING ALGORITHMS CHAPTER 4 CONTENTS 4.1. Introduction ...................................................................................... 80 4.2. Supervised Learning Approach .......................................................... 81 4.3. Unsupervised Learning ..................................................................... 84 4.4. Algorithm Types ................................................................................ 86 References ............................................................................................. 113 Introduction To Algorithms 80 4.1. INTRODUCTION Computational learning theory is the branch of statistics in which the computational analysis and the performance of the machine learning algorithms are studied. In machine learning, such kinds of algorithms are developed that helps the computer to learn. Learning does not essentially involve awareness, finding the statistical symmetries or some patterns in the given data is also learning. Machine learning algorithms hardly resemble a learning task approached by humans. However, in different environments, the machine learning algorithms can provide insight into the comparative difficulty of learning. Based on the preferred result of the algorithm the learning algorithms are ordered into different categories. The common learning algorithm types are: i. Supervised learning: Here a function is generated by the algorithm which maps the inputs to anticipated outputs. The classification problem is one of the standard formulations of this learning. In classification problem the student is required to absorb, to estimate the performance of a function. Out of several classes, the function plots a vector into one by considering several input-output specimens of the function. ii. Unsupervised learning: A set of inputs is modeled by unsupervised learning.
eBook - PDF- Sourabh Pal(Author)
- 2023(Publication Date)
- Arcler Press(Publisher)
MACHINE LEARNING ALGORITHMS CHAPTER6 CONTENTS 6.1. Introduction .................................................................................... 142 6.2. Supervised Learning Approach........................................................ 143 6.3. Unsupervised Learning ................................................................... 147 6.4. Algorithm Types .............................................................................. 150 References ............................................................................................. 180 The Fundamentals of Algorithmic Processes 142 6.1. INTRODUCTION The field of statistics known as computational learning theory studies the computational analysis and performance of machine learning methods. Such algorithms are developed in machine learning to assist the computer in learning. Learning does not always imply consciousness; identifying statistical symmetries or patterns in a set of data is also a form of learning. Human learning algorithms bear no resemblance to machine learning algorithms. Machine learning methods, on the other hand, could provide insight into the relative difficulty of learning in various situations (Figure 6.1). Figure 6.1. Different types of machine learning algorithms. Source: https://towardsdatascience.com/machine-learning-algorithms-in-lay- mans-terms-part-1-d0368d769a7b?gi=3f432d1ebd11. Learning algorithms are classified into multiple groups based on the algorithm’s preferred outcome. The common learning algorithm types are: • Supervised Learning: The algorithm creates a function that maps the inputs to the expected outputs. One of the classic forms of this learning is the categorization issue. The learner must assimilate information to evaluate the performance of a function in a classification issue. The function displays a vector from many classes by considering various input-output specimens of the function.
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