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Financial Machine Learning - Bok av Bryan Kelly - Häftad

Financial Machine Learning - Bok av Bryan Kelly - Häftad

Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.This survey is organized..

SEK 1376.00
1

The Roles and Modes of Human Interactions with Automated Machine Learning Systems - Bok av Thanh Tung Khuat - Häftad

The Roles and Modes of Human Interactions with Automated Machine Learning Systems - Bok av Thanh Tung Khuat - Häftad

Recent years have seen an unprecedented level of technological uptake and engagement by the mainstream. From deepfakes for memes to recommendation systems for commerce, machine learning (ML) has become a regular fixture in society. This ongoing transition from purely academic confines to the general public is not smooth as the public does not have the extensive expertise in data science required to fully exploit the capabilities of ML. As automated machine learning (AutoML) systems continue to progress..

SEK 1356.00
1

An Introduction to Neural Data Compression - Bok av Yibo Yang - Häftad

An Introduction to Neural Data Compression - Bok av Yibo Yang - Häftad

The goal of data compression is to reduce the number of bits needed to represent useful information. Neural, or learned compression, is the application of neural networks and related machine learning techniques to this task. This monograph aims to serve as an entry point for machine learning researchers interested in compression by reviewing the prerequisite background and representative methods in neural compression. Neural compression is the application of neural networks and other machine learning..

SEK 1050.00
1

Ultra-Reliable Low-Latency Communications - Bok av Nurul Huda Mahmood - Häftad

Ultra-Reliable Low-Latency Communications - Bok av Nurul Huda Mahmood - Häftad

Ultra-Reliable Low-Latency Communications (URLLC) was introduced into 5G networks to facilitate machine to machine communication for such applications as the Internet of Things. But designing URLLC systems, with disjointed treatment of the topic in the literature, has proven challenging. In this work, the authors present a comprehensive coverage of the URLLC including the motivation, theory, practical enablers and future evolution. The unified level of details provides a balanced coverage between..

SEK 1390.00
1

Kernel Mean Embedding of Distributions - A Review and Beyond - Bok av Krikamol Muandet - Häftad

Kernel Mean Embedding of Distributions - A Review and Beyond - Bok av Krikamol Muandet - Häftad

This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.

SEK 1456.00
1

Learning with Limited Samples - Bok av Lisha Chen - Häftad

Learning with Limited Samples - Bok av Lisha Chen - Häftad

Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning..

SEK 1340.00
1

Conformal Prediction - Bok av Anastasios N. Angelopoulos - Häftad

Conformal Prediction - Bok av Anastasios N. Angelopoulos - Häftad

Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified..

SEK 1130.00
1

User-friendly Introduction to PAC-Bayes Bounds - Bok av Pierre Alquier - Häftad

User-friendly Introduction to PAC-Bayes Bounds - Bok av Pierre Alquier - Häftad

Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine learning. The proliferation of research has made the field for a newcomer somewhat daunting. In this tutorial, the author guides the reader through the topic's complexity and large body of publications. Covering both empirical and oracle PAC-bounds, this..

SEK 1290.00
1

Universal Features for High-Dimensional Learning and Inference - Bok av Shao-Lun Huang - Häftad

Universal Features for High-Dimensional Learning and Inference - Bok av Shao-Lun Huang - Häftad

In many contemporary and emerging applications of machine learning and statistical inference, the phenomena of interest are characterized by variables defined over large alphabets. This increasing size of both the data and the number of inferences, and the limited available training data means there is a need to understand which inference tasks can be most effectively carriedout, and, in turn, what features of the data are most relevant to them. In this monograph, the authors develop the idea of..

SEK 1340.00
1

AutonoML - Bok av David Jacob Kedziora - Häftad

AutonoML - Bok av David Jacob Kedziora - Häftad

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. This monograph provides an expansive perspective on what constitutes an automated/autonomous ML system. In doing so, the authors survey developments in hyperparameter optimisation,..

SEK 1356.00
1