Microeconomics: Principles, Problems, and Policies, 21st Edition
subject: Economics
creator: Sean Masaki Flynn, Campbell R. McConnell, Stanley L. Brue
publisher: McGraw-Hill
date: 2018
format: PDF
language: English
type: Text

Advanced Macroeconomics, 5th Edition
subject: Economics
creator: David Romer
publisher: McGraw-Hill Education
date: 2018
format: PDF
language: English
type: Text

The Economics, Regulation and Systemic Risk of Insurance Market
subject: Economics
creator: Felix Hufeld, Ralph S. J. Koijen and Christian Thimann
publisher: Oxford University Press
date: 2017
format: PDF
language: English
type: Text

Principles of Economics, 10th Edition
subject: Economics
creator: N. Gregory Mankiw
publisher: Cengage
date: 2024
format: PDF
language: English
type: PDF

International Economics: Theory and Policy, 12th Edition
subject: Economics
creator: Paul Krugman, Maurice Obstfeld, Marc Melitz
publisher: Pearson
date: 2022
format: PDF
language: English
type: Text

Demystifying Global Macroeconomics, 3rd Edition
subject: Economics
creator: John E. Marthinsen
publisher: De Gruyter
date: 2020
format: PDF
language: English
type: Text

Macroeconomics, 11th Edition
subject: Economics
creator: Rudiger Dornbusch, Stanley Fischer, Richard Startz
publisher: McGraw Hill
date: 2011
format: PDF
language: English
type: Text

Economics, 11th Edition
subject: Economics
creator: David Begg, Gianluigi Vernasca, Stanley Fischer, Rudiger Dornbusch
publisher: McGraw-Hill Higher Education
date: 2014
format: PDF
language: English
type: Text

Macroeconomics: Theories and Policies. 10th Edition
subject: Economics
creator: Froyen T. Richard
publisher: Pearson
date: 2014
format: PDF
language: English
type: text

Information-Driven Machine Learning: Data Science as an Engineering Discipline
subject: Intelligence and Data Science Management
description: This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility. While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for a broad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how," this text provides answers to the "why" questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality. Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who d…
creator: Gerald Friedland
publisher: Springer
date: 2023
format: PDF
language: English