If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This week at Microsoft Ignite, a number of new developments to Azure were in focus. While there were dozens of updates to the world's second-largest public cloud, data was once again in the spotlight. The company made a series of announcements to enable users to extract more value from the exponential increase in data. Satya Nadella, in his Ignite keynote, provided a new visionary direction, or at least a new way of expressing the company's cloud endeavors. In short, the Microsoft cloud is evolving to further embrace edge, privacy, security, AI, and developers (both coders and no coders), and to serve as an engine of job creation. On the surface, this shift appears subtle.
Mathematics: If you want to thrive in the field of data science then you need to have a certain familiarity with calculus, probability, linear algebra, and mathematics. Various standard models are essential to construct ML algorithms. In general, a data scientist should know something about probability and statistics theory as the rest depends on the job you apply for. Computer science: It is a study dealing with software systems and includes their theory, development, design, and application. It takes a scientific approach to do computation and carry out its applications. Computer science is considered as a foundation that makes achievements and obtaining more knowledge in the field easier.
High-level classification algorithms focus on the interactions between instances. These produce a new form to evaluate and classify data. In this process, the core is a complex network building methodology. The current methodologies use variations of kNN to produce these graphs. However, these techniques ignore some hidden patterns between attributes and require normalization to be accurate. In this paper, we propose a new methodology for network building based on attribute-attribute interactions that do not require normalization. The current results show us that this approach improves the accuracy of the high-level classification algorithm based on betweenness centrality.
Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called low-level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has a facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as high-level classification.
Author: fchollet Date created: 2020/06/09 Last modified: 2020/06/09 Description: Binary classification of structured data including numerical and categorical features. This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical features. We will use Keras preprocessing layers to normalize the numerical features and vectorize the categorical ones. Note that this example should be run with TensorFlow 2.3 or higher, or tf-nightly.
Klassify Technology, a vendor in Data Classification, Data Discovery and Compliance domain, announced partnership with Bengaluru headquartered Happiest Minds – a next-generation digital transformation, infrastructure, security, and product engineering services company. Under this partnership agreement, Happiest Minds will sell Klassify flagship Data Classification, Data Discovery & Compliance, and Card Data Discovery Suite solutions in APAC, Europe, Middle East, and US markets. Vishal Bindra CEO Klassify said, "We are pleased to welcome Happiest Minds to Klassify Technology elite Partner Club. We continue to expand our reach globally by adding more potential partners to our existing channel base. With this sign up with Happiest Minds we look forward to further increase footprints in global geographies."
In this paper we propose two novel data-level algorithms for handling data imbalance in the classification task: first of all a Synthetic Minority Undersampling Technique (SMUTE), which leverages the concept of interpolation of nearby instances, previously introduced in the oversampling setting in SMOTE, and secondly a Combined Synthetic Oversampling and Undersampling Technique (CSMOUTE), which integrates SMOTE oversampling with SMUTE undersampling. The results of the conducted experimental study demonstrate the usefulness of both the SMUTE and the CSMOUTE algorithms, especially when combined with a more complex classifiers, namely MLP and SVM, and when applied on a datasets consisting of a large number of outliers. This leads us to a conclusion that the proposed approach shows promise for further extensions accommodating local data characteristics, a direction discussed in more detail in the paper.
The imbalanced data classification is one of the most crucial tasks facing modern data analysis. Especially when combined with other difficulty factors, such as the presence of noise, overlapping class distributions, and small disjuncts, data imbalance can significantly impact the classification performance. Furthermore, some of the data difficulty factors are known to affect the performance of the existing oversampling strategies, in particular SMOTE and its derivatives. This effect is especially pronounced in the multi-class setting, in which the mutual imbalance relationships between the classes complicate even further. Despite that, most of the contemporary research in the area of data imbalance focuses on the binary classification problems, while their more difficult multi-class counterparts are relatively unexplored. In this paper, we propose a novel oversampling technique, a Multi-Class Combined Cleaning and Resampling (MC-CCR) algorithm. The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE. It combines it with a simultaneous cleaning operation, the aim of which is to reduce the effect of overlapping class distributions on the performance of the learning algorithms. Finally, by incorporating a dedicated strategy of handling the multi-class problems, MC-CCR is less affected by the loss of information about the inter-class relationships than the traditional multi-class decomposition strategies. Based on the results of experimental research carried out for many multi-class imbalanced benchmark datasets, the high robust of the proposed approach to noise was shown, as well as its high quality compared to the state-of-art methods.
Ridge regression is a well established regression estimator which can conveniently be adapted for classification problems. One compelling reason is probably the fact that ridge regression emits a closed-form solution thereby facilitating the training phase. However in the case of high-dimensional problems, the closed-form solution which involves inverting the regularised covariance matrix is rather expensive to compute. The high computational demand of such operation also renders difficulty in constructing ensemble of ridge regressions. In this paper, we consider learning an ensemble of ridge regressors where each regressor is trained in its own randomly projected subspace. Subspace regressors are later combined via adaptive boosting methodology. Experiments based on five high-dimensional classification problems demonstrated the effectiveness of the proposed method in terms of learning time and in some cases improved predictive performance can be observed.
We study the problem of fast and efficient classification of sequential data (such as time-series) on tiny devices, which is critical for various IoT related applications like audio keyword detection or gesture detection. Such tasks are cast as a standard classification task by sliding windows over the data stream to construct data points. Deploying such classification modules on tiny devices is challenging as predictions over sliding windows of data need to be invoked continuously at a high frequency. Each such predictor instance in itself is expensive as it evaluates large models over long windows of data. In this paper, we address this challenge by exploiting the following two observations about classification tasks arising in typical IoT related applications: (a) the "signature" of a particular class (e.g. an audio keyword) typically occupies a small fraction of the overall data, and (b) class signatures tend to be discernible early on in the data.