Making right decisions for businesses is a constant challenge for professionals. Addressing the issue Mykel J. Kochenderfer, Tim A. Wheeler & Kyle H. Wray has written a book about algorithms for decision making under uncertainty. It discusses various approaches to making decisions when the outcome is not known in advance. The book is intended for advanced undergraduates, graduate students, and professionals. It assumes some mathematical background including multivariable calculus, linear algebra, and probability concepts.
The book covers a wide range of topics related to decision making. It introduces the mathematical formulations of decision making problems and the algorithms to solve them. Examples and exercises are included to help understand the underlying ideas.
Some of the areas where this book can be useful include mathematics, statistics, computer science, aerospace engineering, electrical engineering, and operations research. The book emphasizes algorithms implemented in the Julia programming language. The authors believe this language is ideal for specifying algorithms in a way that is easy to understand.
The focus of this book is on interpretability rather than efficiency. This means the algorithms are designed to be easy to understand rather than to run quickly. Other implementations may be more suitable for industrial applications.
Here are some of the specific topics covered in the book:
Introduction to decision making: This section provides an overview of the field of decision making and the different types of decision problems.
Modeling decision making problems: This section covers how to model decision making problems mathematically. This includes topics such as decision trees, utility theory, and Markov decision processes.
Designing algorithms to make decisions: This section covers different algorithms for making decisions in different types of decision problems. Some of the algorithms covered in the book include decision trees, value iteration, and Q-learning.
Analyzing the performance of decision making algorithms: This section covers how to analyze the performance of decision making algorithms. This includes topics such as expected value, variance, and regret.
You can read the book from the link below.
https://algorithmsbook.com/files/dm.pdf
