• Title: Linear Algebra for Computer Vision, Robotics, and Machine Learning
• Author(s) Jean Gallier and Jocelyn Quaintance
• Publisher: University of Pennsylvania (December 6, 2024)
• Paperback: N/A
• eBook: PDF (787 pages)
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
Jean Gallier J. Quaintance
• Share This:  

Book Description

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.

• Jean Gallier is a researcher in computational logic at the University of Pennsylvania, where he holds appointments in the Computer and Information Science Department and the Department of Mathematics.

• Amazon

• Linear Algebra, Matrix Algebra, and Linear Systems
• Machine Learning
• Computer and Machine Vision
• Robotics and Robot Programming

• Linear Algebra for Computer Vision, Robotics, and Machine Learning (Jean Gallier, et al.)
• The Mirror Site (1) - PDF

• Mathematical Analysis of Machine Learning Algorithms (Tong Zhang)

  This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

• Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

  This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Pen and Paper Exercises in Machine Learning (Michael Gutmann)

  This is a collection of (mostly) pen-and-paper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

• Machine Learning with Neural Networks (Bernhard Mehlig)

  This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

• Dive into Deep Learning (Aston Zhang, et al.)

  This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• Mathematics for Computer Science (Eric Lehman, et al)

  This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. explores the topics of basic combinatorics, number and graph theory, logic and proof techniques.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• Understanding Machine Learning: From Theory to Algorithms

  This book explains the principles behind the automated learning approach and the considerations underlying its usage. It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.