Machine Learning for Computer Scientists and Data Analysts
From an Applied Perspective
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Chapter and Conference Paper
Correction to: Ontology-Driven Scientific Literature Classification Using Clustering and Self-supervised Learning
In the original version of the book, the second author’s first name was inadvertently published with a typo. The name has now been corrected from “Patrck Soong” to “Patrick Soong” in the chapter “Ontology-Driv...
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Chapter and Conference Paper
Ontology-Driven Scientific Literature Classification Using Clustering and Self-supervised Learning
The rapid growth of scientific literature in the fields of computer engineering (CE) and computer science (CS) presents difficulties to researchers who are interested in exploring research publication records ...
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Chapter
SensorNet: An Educational Neural Network Framework for Low-Power Multimodal Data Classification
This chapter presents SensorNet that is a scalable and low-power embedded deep convolutional neural network (DCNN) designed to classify multimodal time-series signals. Time-series signals generated by differen...
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Chapter
Applied Machine Learning for Computer Architecture Security
The ever-increasing complexity of modern computing systems has led to the growth of security vulnerabilities, making such systems appealing targets for increasingly sophisticated cyber-attacks. Cybersecurity f...
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Chapter
What Is Applied Machine Learning?
We begin this chapter by explaining the need to understand data to answer different questions about the distribution of data, important features, how to transform features, how to develop models to handle a ce...
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Chapter
Supervised Learning
This chapter explores many supervised machine learning algorithms that have gained significant popularity in recent applications. Supervised learning is the process of acquiring knowledge about annotated data ...
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Chapter
Reinforcement Learning
The previous chapters discussed different supervised and unsupervised learning, in which cases the data is completely labeled or unlabeled. However, there exist other scenarios where the data is labeled partia...
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Chapter
Recommender Learning
Recommender systems widely exist in a massive number of web applications including news apps, social media platforms, location-based services, and music/video sharing sites. The purpose of recommender systems ...
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Chapter
Adversarial Machine Learning
Advancements in machine learning led to its adoption into numerous applications ranging from computer vision to security. Despite the achieved advancements in the machine learning, the vulnerabilities in those...
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Book
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Chapter
Transfer Learning in Mobile Health
Machine learning is being increasingly utilized in mobile health applications. However, due to considerable variations among sensing platforms, users’ physiological readings, and their behavioral routines, sig...
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Chapter
Applied Machine Learning for Cloud Resource Management
One of the most powerful scale-out infrastructures to perform massive computation and eliminate the need to maintain high-end expensive computing resources at the user side is Cloud. Cloud architectures are va...
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Chapter
A Brief Review of Probability Theory and Linear Algebra
Probability theory is a key building component of machine learning. This chapter explores several facets of probability theory through the use of concrete examples. Numerous subjects are explored, including co...
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Chapter
Unsupervised Learning
In the prior chapter, we explored supervised learning, its algorithms, and applications. For supervised learning, the user must supply the output labels (target), and the machine learning model is supposed to ...
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Chapter
Online Learning
In today’s substantial data era, millions of data points are generated in a matter of seconds, making traditional machine learning algorithms difficult to handle. Online learning approaches strive to update th...
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Chapter
Graph Learning
Transport, chemical structure, physical connection, social networks, and disease spread are all examples of real-world situations where graphs are used. Applying typical deep learning techniques (such as convo...
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Article
A comprehensive study of visual event computing
This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs,...
