A Computational Introduction to Quantum Physics (1版)

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【簡介】 This concise textbook introduces an innovative computational approach to quantum mechanics. Over the course of this engaging and informal book, students are encouraged to take an active role in learning key concepts by working through practical exercises. The book equips readers with some basic methodology and a toolbox of scientific computing methods, so they can use code to simulate and directly visualize how quantum particles behave. The important foundational elements of the wave function and the Schrödinger equation are first introduced, then the text gradually builds up to advanced topics including relativistic, open, and non-Hermitian quantum physics. This book assumes familiarity with basic mathematics and numerical methods, and can be used to support a two-semester advanced undergraduate course. Source code and solutions for every book exercise involving numerical implementation are provided in Python and MATLAB(R), along with supplementary data. Additional problems are provided online for instructor use with locked solutions.

Computational Formalisms in Euclidean Geometry, Vol. I: A Trigonometric, Vectorial and Complex Numbers Approach (1版)

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【簡介】 This book explores three computational formalisms for solving geometric problems. Part I introduces a trigonometric-based formalism, enabling calculations of distances, angles, and areas using basic trigonometry. Part II focuses on complex numbers, representing points in the plane to manipulate geometric properties like collinearity and concurrency, making it particularly useful for planar problems and rotations. Part III covers vector formalism, applying linear algebra to both plane and solid geometry. Vectors are effective for solving problems related to perpendicularity, collinearity, and the calculation of distances, areas, and volumes.Each formalism has its strengths and limitations, with complex numbers excelling in the plane and vectors being more versatile in three-dimensional space. This book equips readers to choose the best approach for various geometric challenges.This book, designed for math majors, especially future educators, is also valuable for gifted high school students and educators seeking diverse proofs and teaching inspiration.

Artificial Intelligence: Foundations of Computational Agents (3版)

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【簡介】 Introduces concepts at the right level of detail, using straightforward language and minimum mathematics Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied Provides two complementary software systems – AIPython and AILog - for experimentation and extension Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises 【目錄】 Part I - Agents in the World Chapter 1 - Artificial Intelligence and Agents Chapter 2 - Agent Architectures and Hierarchical Control Part II - Reasoning and Planning with Certainty Chapter 3 - Searching for Solutions Chapter 4 - Reasoning with Constraints Chapter 5 - Propositions and Inference Chapter 6 - Deterministic Planning Part III - Learning and Reasoning with Uncertainty Chapter 7 - Supervised Machine Learning Chapter 8 - Neural Networks and Deep Learning Chapter 9 - Reasoning with Uncertainty Chapter 10 - Learning with Uncertainty Chapter 11 - Causality Part IV - Planning and Acting with Uncertainty Chapter 12 - Planning with Uncertainty Chapter 13 - Reinforcement Learning Chapter 14 - Multiagent Systems Part V - Representing Individuals and Relations Chapter 15 - Individuals and Relations Chapter 16 - Knowledge Graphs and Ontologies Chapter 17 - Relational Learning and Probabilistic Reasoning Part VI - The Big Picture Chapter 18 - The Social Impact of Artificial Intelligence Chapter 19 - Retrospect and Prospect Appendix A - Mathematical Preliminaries and Notation Appendix B - Mapping to Open-Source Packages