
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title: Machine Learning in Sports: Open Approach for Next Play Analytics
- Author(s) Keisuke Fujii
- Publisher: Springer (2025); eBook (Creative Commons Licensed)
- License(s): Creative Commons License (CC)
- Paperback: 184 pages
- eBook: PDF and ePub
- Language: English
- ISBN-10: 9819614449
- ISBN-13: 978-9819614448
- Share This:
![]() |
This open access book provides cutting-edge work on machine learning in sports analytics, emphasizing the integration of computer vision, data analytics, and machine learning to redefine strategic sports analysis.
About the Authors- N/A
- Machine Learning
- Probability Theory and Stochastic Processes
- Data Analysis and Data Mining
- Artificial Intelligence

- Machine Learning in Sports: Open Approach for Next Play Analytics (Keisuke Fujii)
- The Mirror Site (1) - PDF
-
Probabilistic Machine Learning for Civil Engineers
This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.
-
Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
-
Probabilistic Machine Learning: Advanced Topics (Kevin Murphy)
This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.
-
Foundations of Large Language Models (Tong Xiao, et al.)
This is a book about Large Language Models (LLM). It primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies: pre-training, generative models, prompting techniques, and alignment methods.
-
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.
-
Machine Learning in Production: From Models to Products
This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Packed with real-world examples that take you from start to finish.
-
Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.
-
Multi-Agent Reinforcement Learning (Stefano V. Albrecht, et al.)
The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.
-
Distributional Reinforcement Learning (Marc G. Bellemare, et al)
Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.
-
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.
:
|
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |
|
![]() |