Springer Series in the Data Sciences

Cohesive Subgraph Computation over Large Sparse Graphs

Algorithms, Data Structures, and Programming Techniques

Parametric Approach to Nonparametric Statistics

Deep Learning Architectures

A Mathematical Approach

Deep Learning Architectures

A Mathematical Approach

First-order and Stochastic Optimization Methods for Machine Learning

First-order and Stochastic Optimization Methods for Machine Learning

Mathematical Foundations for Data Analysis

Mathematical Foundations for Data Analysis

Statistics with Julia

Fundamentals for Data Science, Machine Learning and Artificial Intelligence

Statistics with Julia

Fundamentals for Data Science, Machine Learning and Artificial Intelligence

Data Science for Public Policy

Data Science for Public Policy

Statistics in the Public Interest

In Memory of Stephen E. Fienberg

Statistics in the Public Interest

In Memory of Stephen E. Fienberg

Multivariate Data Analysis on Matrix Manifolds

(with Manopt)

Multivariate Data Analysis on Matrix Manifolds

(with Manopt)

Statistical Inference and Machine Learning for Big Data

Statistical Inference and Machine Learning for Big Data

Lectures on the Nearest Neighbor Method

Lectures on the Nearest Neighbor Method

Parametric Approach to Nonparametric Statistics

Continuous Time Modeling in the Behavioral and Related Sciences

Methodological Investigations in Agent-Based Modelling

With Applications for the Social Sciences

Measurement across the Sciences

Developing a Shared Concept System for Measurement

Introduction to Statistics with Python

With Applications in the Life Sciences

Measurement Across the Sciences

Developing a Shared Concept System for Measurement

Measurement Across the Sciences

Developing a Shared Concept System for Measurement

Using R for Introductory Statistics in Social Sciences

Model-Free Prediction and Regression

A Transformation-Based Approach to Inference