Introduction to Machine Learning for the Sciences
by Titus Neupert, et al.
Publisher: arXiv.org 2021
Number of pages: 80
Description:
This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich and the Quantum Matter and AI group at the Delft University of Technology. We discuss supervised, unsupervised, and reinforcement learning.
Download or read it online for free here:
Download link
(4.1MB, PDF)
Similar books
Machine Learning for Designersby Patrick Hebron - O'Reilly Media
This book introduces you to contemporary machine learning systems and helps you integrate machine-learning capabilities into your user-facing designs. Patrick Hebron explains how machine-learning applications can affect the way you design websites.
(9818 views)
A Brief Introduction to Machine Learning for Engineersby Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(9508 views)
Inductive Logic Programming: Theory and Methodsby Stephen Muggleton, Luc de Raedt - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(40412 views)
Boosting: Foundations and Algorithmsby Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(8894 views)