Machine Learning by Tom M. Mitchell (1997-03-01) https://www.amazon.com/gp/product/B01FIYYP6W?pf_rd_p=c2945051-950f-485c-b4df-15aac5223b10&pf_rd_r=7WB8PHPHB10Z2C4A7H64

Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. Before starting on this programming exercise, we strongly recom- mend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise. You can also find instructions for installing Octave/MATLAB in the “En- vironment Setup Instructions” of the course website. Files included in this exercise ex1.m - Octave/MATLAB script that steps you through the exercise ex1 multi.m - Octave/MATLAB script for the later parts of the exercise ex1data1.txt - Dataset for linear regression with one variable ex1data2.txt - Dataset for linear regression with multiple variables submit.m - Submission script that sends your solutions to our servers [?] warmUpExercise.m - Simple example function in Octave/MATLAB [?] plotData.m - Function to display the dataset [?] computeCost.m - Function to compute the cost of linear regression [?] gradientDescent.m - Function to run gradient descent [†] computeCostMulti.m - Cost function for multiple variables [†] gradientDescentMulti.m - Gradient descent for multiple variables [†] featureNormalize.m - Function to normalize features [†] normalEqn.m - Function to compute the normal equations ? indicates files you will need to complete † indicates optional exercises

手势识别中所用到的proto file，用于基于OpenCV实现对手势的识别。 具体代码可参考：https://cchang.blog.csdn.net/article/details/87979328

Computational Interaction ISBN-10 书号: 0198799616 ISBN-13 书号: 9780198799610 出版日期: 2018-04-08 pages 页数: (432) Edited by ANTTI OULASVIRTA Associate Professor Aalto University PER OLA KRISTENSSON University Reader in Interactive Systems Engineering University of Cambridge XIAOJUN BI Assistant Professor Stony Brook University ANDREW HOWES Professor and Head of School at the School of Computer Science University of Birmingham This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design m

scikit-learn 0.19.2官方教程，英文版。以Python语言开发的机器学习库，简单易用，实例丰富。

吴恩达老师的机器学习课程的习题答案，用matlab实现。

谷歌推荐学习tensorflow的代码例子，目前来说还是最新的

机器学习经典论文，都为英文文献，英文吃力的请慎重。积分太高了改低一点，有积分，积分不求多，还要五十字

Language: English Format: PDF Year: 2018 Pages: 161 ISBN : 3030001288 This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.

Introduction to Machine Learning with Python 英文版