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
Information Theory, Inference, and Learning Algorithmsby David J. C. MacKay - Cambridge University Press
A textbook on information theory, Bayesian inference and learning algorithms, useful for undergraduates and postgraduates students, and as a reference for researchers. Essential reading for students of electrical engineering and computer science.
(30119 views)
A Course in Machine Learningby Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
(22913 views)
Practical Artificial Intelligence Programming in Javaby Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(25709 views)
Introduction to Machine Learningby Alex Smola, S.V.N. Vishwanathan - Cambridge University Press
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
(10380 views)