随着计算机科学与技术的不断发展，C语言作为一门经典且强大的编程语言，在学术界与工业界都占有重要地位。北京航空航天大学作为中国知名的理工科高等学府，其内部教育资源的质量自然备受关注。近期，该校推出的“北航C语言PPT”课件，为计算机编程爱好者特别是初学者提供了一条通向C语言编程世界的捷径。 本课件适合完全没有编程基础的初学者，通过直观的教学设计和严谨的知识结构，能够帮助学习者从零开始，逐步掌握C语言的核心知识点。在课件的伊始，学习者将接触C语言的基本元素，包括变量、常量和运算符等概念。随后，课程将逐步引导学习者深入了解C语言的控制结构、函数、数组、指针等高级话题。掌握这些知识，对于后续学习数据结构和算法具有重要的铺垫作用。 对于那些需要为数据结构理论考试做准备的学生而言，这份课件同样具有很高的价值。在课件中，学生不仅能学习到C语言编程的基础知识，还会接触到链表、树、图、堆栈、队列等数据结构的详细讲解和应用实例。这些内容对于理解和掌握数据结构的原理和实现至关重要，不仅有助于考试复习，更能为实际编程问题的解决提供思路。 “北航C语言PPT”中的“C语言程序设计”部分，更是强调了编程技巧与习惯的养成。从良好的代码风格、编程规范到错误处理和调试技巧，本课件全面覆盖了C语言编程过程中可能遇到的方方面面。这些内容的学习对于培养一个优秀程序员的素质至关重要，能够帮助学习者养成系统化、逻辑化的编程思维，为日后的编程实践打下坚实的基础。 从文件名“北航数据结构”可以窥见，该课件中还包含了一系列对数据结构深入讲解的PPT。这部分内容可能涉及数组、链表、栈、队列、树、图等数据结构的基本概念和操作，以及它们在C语言中的实现方法。此外，排序和查找是数据结构课程中不可或缺的部分，通过课件中的教学，学习者将能够掌握冒泡排序、快速排序、归并排序等排序算法，以及顺序查找、二分查找等查找算法的原理和应用。 除了理论知识的传授，北京航空航天大学的教师们还可能在课件中提供大量的编程实践案例。这些案例有助于学习者将理论知识转化为实践技能，通过亲自编写和调试代码，体验从问题提出到问题解决的完整过程。在此过程中，学习者不仅能够提高解决实际问题的能力，还能够加深对数据结构和C语言的理解。 总而言之，“北航C语言PPT”是一个系统而全面的编程入门与提升资源，其内容涵盖了从C语言基础语法到数据结构的深入讲解，非常适合那些希望从零开始学习编程或希望巩固数据结构理论知识的读者。通过本课件的学习，学习者将能够建立起扎实的编程基础，掌握数据结构的核心概念与应用技巧，并为未来在更高级别编程语言和软件开发领域的学习和工作打下坚实的基础。

Computer Vision: Principles, Algorithms, Applications, Learning By 作者: E. R. Davies ISBN-10 书号: 012809284X ISBN-13 书号: 9780128092842 Edition 版本: 5 出版日期: 2017-11-29 pages 页数: (900 ) Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications―including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians―give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples―code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)

包含八个代码文件，包括：特征抽取，特征选择，标准化，归一化，PCA，还有一些sklearn流行数据集的使用方法，以及kaggle大赛上的一个项目的数据分析阶段

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) ISBN-10 书号: 1138744387 ISBN-13 书号: 9781138744387 Edition 版本: 1 出版日期: 2018-04-04 pages 页数: 418 Chapter 1 Preliminaries and Overview Guozhu Dong and Huan Liu Part I Feature Engineering for Various Data Types Chapter 2 Feature Engineering for Text Data Chase Geigle, Qiaozhu Mei, and ChengXiang Zhai Chapter 3 Feature Extraction and Learning for Visual Data Parag S. Chandakkar, Ragav Venkatesan, and Baoxin Li Chapter 4 Feature-Based Time-Series Analysis Ben D. Fulcher Chapter 5 Feature Engineering for Data Streams Yao Ma, Jiliang Tang, and Charu Aggarwal Chapter 6 Feature Generation and Feature Engineering for Sequences Guozhu Dong, Lei Duan, Jyrki Nummenmaa, and Peng Zhang Chapter 7 Feature Generation for Graphs and NetworksYuan Yao, Hanghang Tong, Feng Xu, and Jian Lu Part lI General Feature Engineering Techniques Chapter 8 Feature Selection and Evaluation Yun Li and Tao Li Chapter 9 Automating Feature Engineering in Supervised Learning Udayan Khurana Chapter 10 Pattern-Based Feature Generation Yunzhe Jia, James Bailey, Ramamohanarao Kotagiri, and Christopher Leckie Chapter 11 Deep Learning for Feature Representation Suhang Wang and Huan Liu Part ll Feature Engineering in Special Applications Chapter 12 Feature Engineering for Social Bot Detection Onur Varol, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini Chapter 13 Feature Generation and Engineering for Software Analytics Xin Xia and David Lo Chapter 14 Feature Engineering for Twitter-Based Applications Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan Index

multidimensional particle swarm optimization for machine learning and pattern recognition

Understanding Machine Learning - From Theory to Algorithms这本书的中文扫描版

Apress出版, 2019年的书。全英文。我还没看，无法发表意见。请自己到Amazon看介绍.

Computers have gained a cardinal place in modern societies, thanks to higher efficiencies and miniaturisation. However, their dramatic progress will soon have to stop as the limits of miniaturisation are being reached. Furthermore, few people realise that those computers are, in fact, not as powerful as they seem to be. And while the world champion at Go lost to a computer, an average human still beats a computer at relatively easy tasks such as recognising an object in a picture. Artificial intelligence is the key to more versatile computing machines capable of solving such challenging tasks.

《机器视觉算法与应用(双语版)》是一本关于机器视觉算法与应用的中英文对照版教材。而且附带有halcon代码，讲解十分详细！