Data Understanding, Data Analysis, Data Science (Course Notes)

  Data Understanding, Data Analysis, Data Science

   Volume 3: Spotlight on Machine Learning

     Data Understanding, Data Analysis, Data Science

      468 pages | July 2024 | Quadrangle
      Preface | Contents | Index | Datasets

      DUDADS Volumes: [1 | 2 | 3 | 4 | 5 | PDV]

      Idlewyld Analytics and Consulting Services    Data Action Lab
Chapter 19: Introduction to Machine Learning (80 pages)
      P. Boily
Chapter 20: Regression and Value Estimation (100 pages)
      P. Boily
Chapter 21: Focus on Classification and Supervised Learning (112 pages)
      P. Boily (with contributions from O. Leduc and S. Hagiwara)
Chapter 22: Focus on Clustering (94 pages)
      J. Schellinck and P. Boily (with contributions from A. Maheshwari)
Chapter 23: Feature Selection and Dimension Reduction (66 pages)
      P. Boily (with contributions from O. Leduc, A. Macfie, A. Maheshwari, and M. Pelletier)

Back Cover Description: "Volume 3 of Patrick Boily's comprehensive series, Data Understanding, Data Analysis, and Data Science, Spotlight on Machine Learning, takes a turn into the relatively recent world of machine learning, guiding the readers through the intricacies of computational algorithms that teach computers how to learn from data.

Beginning with a overview of machine learning, Volume 3 covers critical notions such as regression, supervised learning techniques, the (sometimes art of) clustering, and the crucial (and often neglected) topics of feature selection and dimension reduction.

What sets this volume apart is its commitment to a tool-agnostic approach, emphasizing understanding over mere implementation, consistent with the ethos of the series. Aspiring machine learning engineers, data science practitioners, and readers fascinated by the ability of algorithms to obtain actionable insights from data will find this volume a rigorous yet accessible guide into the complexities of machine learning.

Optimized for parallel reading alongside guided lectures or as an invaluable companion for independent study, Spotlight on Machine Learning builds on the foundation laid in Volumes 1 and 2, offering a focused understanding of machine learning's role in the broader field of data science and providing readers with the knowledge required to navigate this rapidly evolving landscape and uncover the connections that link data, algorithms, and insight."