We are delighted to introduce the proceedings of the second edition of the 2017 European Alliance for Innovation (EAI) International Conference on Machine Learning and Intelligent Communications (MLICOM). This conference brought together researchers, developers, and practitioners from around the world who are leveraging and developing machine learning and intelligent communications. The technical program of MLICOM 2017 consisted of 141 full papers in oral presentation sessions at the main conference tracks. The conference tracks were: Main Track, Machine Learning; Track 1, Intelligent Positioning and Navigation; Track 2, Intelligent Multimedia Processing and Security; Track 3, Intelligent Wireless Mobile Network and Security; Track 4, Cognitive Radio and Intelligent Networking; Track 5, Intelligent Internet of Things; Track 6, Intelligent Satellite Communications and Networking; Track 7, Intelligent Remote Sensing, Visual Computing and Three-Dimensional Modeling; Track 8, Green Communication and Intelligent Networking; Track 9, Intelligent Ad-Hoc and Sensor Networks; Track 10, Intelligent Resource Allocation in Wireless and Cloud Networks; Track 11, Intelligent Signal Processing in Wireless and Optical Communications; Track 12, Intelligent Radar Signal Processing; Track 13, Intelligent Cooperative Communications and Networking. Aside from the high-quality technical paper presentations, the technical program also featured three keynote speeches. The three keynote speeches were by Prof. Haijun Zhang from the University of Science and Technology Beijing, China, Prof. Yong Wang from Harbin Institute of Technology, China, and Mr. Lifan Liu from National Instruments China. Coordination with the steering chairs, Imrich Chlamtac, Xuemai Gu, and Gongliang Liu, was essential for the success of the conference. We sincerely appreciate their constant support and guidance. It was also a great pleasure to work with such an excellent Organizing Committee who worked hard to organize

2*2 mimo 系统 的检测算法的matlab仿真，包括ml.zf.mmse等多种算法的实现以及ber性能曲线图

利用大恒DH-HV1351UM-ML数字摄像机设备进行单帧图像采集保存,VC单文档，源代码和程序，大恒DH-HV1351UM-ML数字摄像机usblib和头文件

最大似然方法，亲测有效，对于正在学习最大似然相关方面的同学很有帮助，欢迎下载

移动机器人路径规划与运动控制 移动机器人路径规划与运动控制

handson-ml-master.zip 个人觉得挺不错的 实践必备的一本书

SAP 物料分类账，配置到实际操作，比零碎收集资料有用

Learning TensorFlow by Tom Hope, Yehezkel S. Resheff, and Itay Lieder Copyright © 2017 Tom Hope, Itay Lieder, and Yehezkel S. Resheff. All rights reserved. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students, and researchers. The book adopts a hands-on approach suitable for a broad technical audience, allowing beginners a gentle start while diving deep into advanced topics and showing how to build productionready systems.

spark Mllib 协同过滤测试数据包含一部分用户对电影的评分数据（用于测试）

1-15节全部完整版讲义！超清分享~~~（附赠目录索引和NG原版讲义） 含金量高，独家整理~~ 目录如下： 公开课笔记1-2——线性规划、梯度下降、正规方程组 公开课笔记3——局部加权回归、逻辑斯蒂回归、感知器算法 公开课笔记4——牛顿方法、指数分布族、广义线性模型 公开课笔记5——生成学习、高斯判别、朴素贝叶斯 公开课笔记6——NB多项式模型、神经网络、SVM初步 公开课笔记7——最优间隔分类、原始/对偶问题、SVM对偶 公开课笔记8———核技法、软间隔分类器、SMO算法 公开课笔记9—偏差/方差、经验风险最小化、联合界、一致收敛 公开课笔记10——VC维、模型选择、特征选择 公开课笔记11——贝叶斯正则化、在线学习、ML应用建议 公开课笔记12——K-Means、混合高斯分布、EM算法 公开课笔记13A——混合高斯模型、混合贝叶斯模型 公开课笔记13B-因子分析模型及其EM求解 公开课笔记14——主成分分析 公开课笔记15—隐含语义索引、奇异值分解、独立成分分析