YSDA自然语言处理课程 这是2020年版本。 有关上一年的课程资料，请转到 每周的讲座和研讨会资料位于./week*文件夹中，有关资料和说明，请参阅README.md YSDA作业的最后期限将在Anytask中列出（）。 任何技术问题，想法，课程材料中的错误，贡献想法-添加 安装库和故障排除：。 教学大纲 词嵌入 讲座：单词嵌入。 分布语义。 基于计数的（神经前）方法。 Word2Vec：学习向量。 GloVe：先数一数然后学习。 评价：内在性与外在性。 分析和可解释性。 研讨会：玩单词和句子的嵌入 作业：基于嵌入的机器翻译系统 文字分类 讲座：文本分类：简介和数据集。 通用框架：特征提取器+分类器。 经典方法：朴素贝叶斯，MaxEnt（逻辑回归），SVM。 神经网络：通用视图，卷积模型，递归模型。 实用技巧：数据增强。 分析和可解释性。 研讨会：使用卷积神经网络进行文本分类。

A First Course in Mathematical Analysis Mathematical Analysis (often called Advanced Calculus) is generally found by students to be one of their hardest courses inMathematics. This text uses the so-called sequential approach to continuity, differentiability and integration to make it easier to understand the subject. Topics that are generally glossed over in the standard Calculus courses are given careful study here. For example, what exactly is a ‘continuous’ function? And how exactly can one give a careful definition of ‘integral’? This latter is often one of the mysterious points in a Calculus course – and it is quite tricky to give a rigorous treatment of integration! The text has a large number of diagrams and helpful margin notes, and uses many graded examples and exercises, often with complete solutions, to guide students through the tricky points. It is suitable for self study or use in parallel with a standard university course on the subject.

A first course in probability 清晰版，非扫描版。

A First Course in Machine Learning, Second Edition (Machine Learning & Pattern Recognition) by Simon Rogers, Mark Girolami 2016 | ISBN: 1498738486 | English | 427 pages | PDF | 161 MB "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." ―Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." ―Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." ―Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." ―David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." ―Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." ―Guangzhi Qu, Oakland University, Rochester, Michigan, USA

A First Course in Machine Learning MATLAB 2009

在线考试系统 基于Spring Boot设计一个在线考试系统，实现线上巩固和应用以及检测相结合。相比于传统的线下考试，为更多的考试和参与考试的相关人员提供更多的便利，可以在线上即可实现考试和检测，无需再到线下考试，而批改任务也将大大地优化，提高教育行业工作者的效率，以及对于传统教学的优势互补，同时增强教学管理质量，提高教学效率，实现高效互动。 应用功能结构 系统ER图 项目用到的技术 项目采用前后端分离开发。 SpringBoot2.1.6 Mybatis Mysql Redis druid mybatis generator HTML JQuery Bootstrap 应用截图 登录界面 注册界面 在线评卷 人员管理 考试管理 题目管理 项目部署 开发项目环境说明： 系统：Windows10 jdk版本：1.8 IntelliJ IDEA 版本：2.5 1.还原数据库文件 运行Mysql

Calculator Developing iOS 8 Apps with Swift / Swift iOS 8 应用开发. Calculator written in Swift. Stanford Open Course on iTunes U. iTunes U 斯坦福大学开放课程, 计算器实例中文版，和英文版功能相同。 Swift 本身支持中文的变量和方法，本程序可直接运行。 (含第五讲新增部分) License MIT License.

功能磁共振成像数据分析课程 fMRI数据分析课程的材料由位于托伦的，面向认知科学专业的学生（包括一年级学生）。 该课程涵盖fMRI数据分析的基本主题（使用Python）。 霍格沃茨之路 我称这门课程的教学方式为霍格沃茨方式。 它指的是，对于大多数参与者而言，fMRI数据分析，python编程，版本控制等在课程开始之初都是不可思议的事实。 该课程的目的是介绍功能磁共振成像数据分析，可再现性，Python和版本控制等主题，从而为学生提供许多乐趣和自由。 该课程的最重要目标是减少最初尝试尝试全新事物的恐惧，这实际上是相当复杂的。 :warning: 内容警告：该课程包含许多不适合麻瓜和过分认真的人的“哈利波特”内容。 如何成为神经信息学家？ 一般信息 该课程得到了和。 时间 授课与练习：30小时（每两周分为3小时的课程） 作业（Jupyter Notebooks通过GitHub课堂共享，DataCam

advanced_fMRI_course：用于UoB（英国）的fMRI数据分析课程的资源和实践清单（2014-2017）

A COURSE IN ROBUST CONTROL （经典控制书）