Search results for Communications and signal processing

5 - Clustering methods
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

Published online:

17 December 2025

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17 July 2025, pp 111-130
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Summary

In this chapter we present some of the most popular approaches to clustering, and discuss general techniques for evaluating and validating the quality of a data partition.

12 - Beyond the mean/variance model
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 311-336
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Summary

In this chapter we explore several aspects in portfolio allocation that go beyond the classical single-period mean/variance model discussed in Chapter 11.

14 - Text analytics
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 371-396
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Summary

This chapter introduces the basic formalism of representing text, and looks at widely used techniques in the analysis of textual data such as topic modeling, language modeling, and classification.

3 - Data models and estimation
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 31-80
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Summary

This chapter introduce a basic statistical models for static and dynamic data generation, and discusses classical Bayesian approach for the estimation of the parameters of the model.

Contents
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp vii-xii
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6 - Linear regression models
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 131-164
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Summary

This chapter introduces linear regression, the workhorse of statistics and (supervised) learning, which relates an input vector to an output response by means of linear combination.

13 - Financial networks
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 337-370
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Summary

This chapter introduces the basic terminology and formalism on graph theory. Next, we introduce various types of networks that are of interest in finance.

2 - Data representation and visualization
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 7-30
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Summary

This chapter introduces the representation and organization of data. We illustrate standard preliminary data manipulation and visualization techniques.

Reviews
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp ii-ii
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4 - Principal component analysis and low-rank approximation
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 81-110
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Summary

In this chapter, we introduce principal component analysis (PCA), a common practice to reduce its dimensionality, and discuss the link between PCA and low-rank approximations.

8 - Nonlinear classifiers and kernel methods
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 193-214
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Summary

This chatper first introduces the kernel trick, which allows us to operate in the original lower-dimensional domain. We then discuss decision tree and ensemble methods for reducing data over-fitting.

10 - Optimization tools
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 245-272
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Summary

This chapter introduces the numerical convex optimization problem that minimize a certain objective function subject to some constraints. We also introduce an efficient algorithm for solving such problems.

11 - Mean/variance portfolio optimization
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 273-310
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Summary

This chapter introduces the classical mean/variance portfolio design approach, and discusses extensions of the basic model, including transaction costs, market impact, and risk beyond the variance.

1 - Preface
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 1-6
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Summary

This chapter provides an overview of the topics covered in this, the book’s structure, the scope and presentation of the books, and the target audience for the book.

Index
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 397-400
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Dedication
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp v-vi
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9 - Neural networks and deep learning
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 215-244
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Summary

In this chapter we introduce the main concepts of neural networks (NNs). Next, we present the main building blocks of a neural network and we discuss the most common training techniques.

7 - Linear classifiers
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp 165-192
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Summary

In this chapter, we focus on linear classifiers such as logistic regression, and linear support vector machines. We then extend to the multi-class case and discuss issues of regularization, sparsity, robustness, and class imbalanace.

Copyright page
Giuseppe Calafiore, Politecnico di Torino, Italy, and VinUniversity, Hanoi, Laurent El Ghaoui, VinUniversity, Vietnam, Giulia Fracastoro, Politecnico di Torino, Alicia Tsai, University of California, Berkeley
Book:

Financial Data Science

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17 December 2025

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17 July 2025, pp iii-iv
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References
Le Liang, Southeast University, Nanjing, Shi Jin, Southeast University, Nanjing, Hao Ye, University of California, Santa Cruz, Geoffrey Ye Li, Imperial College of Science, Technology and Medicine, London
Book:

Wireless Communications and Machine Learning

Published online:

21 October 2025

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03 July 2025, pp 274-286
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Communications and signal processing

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6829 results in Communications and signal processing

5 - Clustering methods

Summary

12 - Beyond the mean/variance model

Summary

14 - Text analytics

Summary

3 - Data models and estimation

Summary

Contents

6 - Linear regression models

Summary

13 - Financial networks

Summary

2 - Data representation and visualization

Summary

Reviews

4 - Principal component analysis and low-rank approximation

Summary

8 - Nonlinear classifiers and kernel methods

Summary

10 - Optimization tools

Summary

11 - Mean/variance portfolio optimization

Summary

1 - Preface

Summary

Index

Dedication

9 - Neural networks and deep learning

Summary

7 - Linear classifiers

Summary

Copyright page

References

Communications and signal processing

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6829 results in Communications and signal processing

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