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- Title: Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning
- Author(s) Jean Gallier and Jocelyn Quaintance. Seongjai Kim
- Publisher: University of Pennsylvania (2025)
- Paperback: N/A
- eBook: PDF (2204 pages)
- Language: English
- ISBN-10/ASIN: N/A
- ISBN-13: N/A
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Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!
About the Author(s)- 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.
- Machine Learning
- Applied Mathematics
- Algebra, Abstract and Linear Algebra, etc.
- Calculus and Mathematical Analysis
- Statistics
- Probability and Stochastic Processes

- Algebra, Topology, Differential Calculus, and Optimization for Computer Science and Machine Learning
- The Mirror Site (1) - PDF
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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.
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Mathematical Foundations of Machine Learning (Seongjai Kim)
This book delves into the fundamental mathematical concepts that underpin the field of machine learning, providing a comprehensive exploration of the mathematical principles behind algorithms and models.
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Mathematical Foundations of Reinforcement Learning (Shiyu Zhao)
This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic Reinforcement Learning (RL) algorithms. Numerous illustrative examples are included throughout.
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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.
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Mathematical Engineering of Deep Learning (Benoit Liquet, et al.)
This book provides a complete and concise overview of deep learning using the language of mathematics - a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning.
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Mathematical Theory of Deep Learning (Philipp Petersen, et al.)
This book provides an introduction to the mathematical analysis of deep learning. It covers fundamental results in approximation theory, optimization theory, and statistical learning theory, which are the three main pillars of deep neural network theory.
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Linear Algebra for Computer Vision, Robotics, and Machine Learning
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.
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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.
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