Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
References 395 Index 407 c 2020 M. P. Deisenroth, A. A. Faisal, C. S. Ong. To be published by Cambridge University Press.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering. Hardback
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of "black art" that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
Information has become an important commercial commodity-indeed, possibly the most important product of the future. While we have well-developed technologies to store data, the analysis to extract information is time-consuming and requires skilled human intervention. Machine learning algorithms augment statistical analysis by providing mechanisms that automate the information discovery process. These algorithms also tend to be more accessible to end-users and domain experts. The two analysis methods are converging, and the fields have much to offer each other.
International Journal of Scientific Research in Science, Engineering and Technology, 2021
While dealing with machine learning, a computer learns first to perform a roles/task by learning a set of training examples. The computer performs then the same task along with data it hasn't found before. This paper presents a brief overview of machine-learning types along with instances. The paper also covers differences between supervised and unsupervised learning.
2013
Machine learning [1] is concerned with algorithmically finding patterns and relationships in data, and using these to perform tasks such as classification and prediction in various domains. We now introduce some relevant terminology and provide an overview of a few sorts of machine learning approaches.
The process of creating machine learning algorithms. This paper delivers the base knowledge needed to understand what machine learning is, the techniques it uses and a look inside the concepts that are required. The process detailed was taken from EliteDataScience's Free 7 Day Crash Course and was re-explained in my own words with some additional knowledge on the concepts explained. The paper includes a brief section on neural networks and why they are used in machine learning today.
Deep Learning in Computational Mechanics, 2021
Nowadays, machine learning is arguably the most successful and widely used technique to address problems that cannot be solved by hand crafted programs. In contrast to conventional algorithms following a predefined set of rules, a machine learning algorithm relies on a large amount of data that is observed in nature, handcrafted by humans, or generated by another algorithm [Bur19]. A more formal definition by Mitchell states that "a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E" [Mit97]. Taking image recognition as an example, the task T is to classify previously unseen images, the performance measure P corresponds to the amount of correctly classified images, and the experience E includes all images that have been used to train the algorithm. Most machine learning algorithms can be decomposed into the following features: a dataset, a cost function, an optimization procedure, and a parameterized model [GBC16]. Generally, the cost function defines an optimization criterion by relating the data to the model parameters. Further, the optimization procedure searches for the model parameters representing the provided data best. The key difference between machine learning and solving an optimization problem is that the optimized model is then used for predictions on previously unseen data.
Journal of Physics: Conference Series, 2018
The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which is accelerating the adoption of machine learning solutions& practices. Machine Learning enables computers to imitate and adapt human-like behaviour. Using machine learning, each interaction, each action performed, becomes something the system can learn and use as experience for the next time. This work is an overview of this data analytics method which enables computers to learn and do what comes naturally to humans, i.e. learn from experience. It includes the preliminaries of machine learning, the definition, nomenclature and applications' describing it's what, how and why. The technology roadmap of machine learning is discussed to understand and verify its potential as a market & industry practice. The primary intent of this work is to give insight into why machine learning is the future.
Deleted Journal, 2023
The domain of machine learning has experienced an unparalleled increase in attention and implementation, becoming an essential component of diverse businesses. This review paper provides a thorough analysis of the comprehensive handbook named "Machine Learning Basics: A Comprehensive Guide." Written by [Dr. Jane Doe], this guide has become a vital reference for those at all levels of expertise seeking to comprehend and traverse the intricate realm of machine learning.
This report reviews the 31 papers on machine learning that were presented at the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85) held in Los Angeles during August, 1985. The papers are grouped according to a taxonomy of the various subareas of machine learning research. The areas receiving the most attention at IJCAI-85 included learning apprentice systems and methods of explanation-based learning, although virtually all areas of machine learning research were represented. The paper describes some opportunities for further research, especially in the area of discovering new terms. The wide variety and high quality of the papers demonstrates that machine learning is a very healthy field of research.
Applied Sciences, 2022
Machine learning (ML) is one of the most exciting fields of computing today [...]
IJCSMC, 2019
The research paper talks about the need, importance, features and emergence of Machine Learning.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.