My_continuously_updated_Machine_Learning,_Probabil_machine-learx

# Live Machine Learning Class: ### 中文机器学习研究线上课 2022年我坚持每周日晚上8:30直播机器学习研究课程系列 ([微信二维码在这个链接](https://github.com/roboticcam/machine-learning-notes/blob/master/files/class_qrcode.jpg))- From 2022, I hold regular 8:30pm Sunday Night live (SNL) broadcast on Machine Learning theory. ### English version From April 2022, I started a machine learning research seminar series every 2-3 weeks in English via Zoom. It's at 7pm Hong Kong Time. I will continue to explain machine learning using an intermediate level mathematics. The current topic is: "Gradient Descend Research". You need a solid understanding of linear algebra, calculus, probability and statistics. You can register via meetup https://www.meetup.com/machine-learning-hong-kong/ (Back in Australia, I also conducted research training to all machine learning PhD students at Australian universities, with over 100 students participating via Zoom.) # Learning Theory Classes * ### [Class 1: Introduction](https://github.com/roboticcam/machine-learning-notes/blob/master/files/1.introduction.pdf) ### * ### [Class 2: Concentration Inequality](https://github.com/roboticcam/machine-learning-notes/blob/master/files/2.concentration_inequality.pdf) ### * ### [Class 3: Rademarcher Complexity](https://github.com/roboticcam/machine-learning-notes/blob/master/files/3.rademarcher.pdf) ### * ### [Class 4: Neural Tangent Kernel](https://github.com/roboticcam/machine-learning-notes/blob/master/files/4.ntk.pdf) ### * ### [Class 5: PAC Bayes](https://github.com/roboticcam/machine-learning-notes/blob/master/files/5.pac_bayes.pdf) ### * ### [Class 6: Johnson–Lindenstrauss lemma](https://github.com/roboticcam/machine-learning-notes/blob/master/files/j_l_lemma.pdf) ### # Video Tutorial to these notes 视频资料 * I recorded about 20% of these notes in videos in 2015 in Mandarin (all my notes and writings are in English) You may find them on [Youtube](https://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q) and [bilibili](https://space.bilibili.com/327617676) and [Youku](http://i.youku.com/i/UMzIzNDgxNTg5Ng) 我在2015年用中文录制了这些课件中约10％的内容 (我目前的课件都是英文的)大家可以在[Youtube](https://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q) [哔哩哔哩](https://space.bilibili.com/327617676) and [优酷](http://i.youku.com/i/UMzIzNDgxNTg5Ng) 下载 # Course on Foundational Mathematics in Machine Learning 机器学习基础数学课程 * ### [Class 1: Model Evaluation](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_model_evaluation.pdf) ### common concepts and techniques for classification model evaluation, including bootstrapping sampling, confusion matrices, receiver operating characteristic (ROC) curves. 分类模型评估的常见概念和技术，包括自举抽样、混淆矩阵、接收器操作特征 (ROC) 曲线 * ### [Class 2: Decision Tree](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_decision_tree.pdf) ### In addition to all the basics of decision trees, I've added a $\chi^2$ test section to this note. 除了决策树的所有基础知识之外，我还在此说明中添加了 $\chi^2$ 测试部分。 * ### [Class 3: Simple Bayes](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_simple_bayes.pdf) ### This note is intended to provide an intuitive explanation of the basic concepts of probability, Bayes' theorem, graphical models of probability. 本课件旨在对概率的基本概念、贝叶斯定理、概率的图形模型提供直观的解释 * ### [Class 4: Regression](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_regression.pdf) ### This note is to explain the century-old, simplest regression models: linear and polynomial regression, and some techniques for evaluating regression performance, especially the coefficient of determination (CoD) method. 这篇笔记是为了解释最简单的回归模型：线性回归和多项式回归，以及一些评估回归性能的技术，尤其是确定系数 (CoD) 方法 * ### [Class 5: Neural Network](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_neural_network.pdf) ### First I show three different last output layer models: logistic, multinomial, and linear regression. Then I show the concept of gradient descent. The main part is to show a basic fully connected neural network and finally a convolutional neural network. 首先，我展示了三个不同的最后输出层模型：逻辑回归、多项式和线性回归。然后我展示了梯度下降的概念。主要部分是展示一个基本的全连接神经网络，最后是一个卷积神经网络。 * ### [Class 6: Unsupervised Learning](https://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_unsupervised.pdf) ### This note describes some common topics in unsupervised learning. From the most obvious methods like clustering, to topic modeling (Latent Diricher Allocation) and traditional word embeddings like the word2vec algorithm. 本课件描述了无监督学习中的一些常见主题。从最明显的方法（如聚类）到主题建模和传统的词嵌入（如 word2vec 算法）。 # Course on Intemediate Mathematics in Machine Learning 机器学习中级数学课程 I'm currently updating/validating and correcting notes I've written over the past decade and incorporating them into an introduction/intermediate/advanced machine learning course. I will gradually delete all my previous Beamer notes and replace them with technical report notes. 我目前正在更新/验证和更正我在过去十年中写的笔记，并将它们合并到入门/中级/高级机器学习课程中。 我会逐渐删除之前写的 Beamer 笔记，并用技术报告笔记代替它们。 * ### [Expectation Maximization](https://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_em.pdf) ### Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model, **[[gmm_demo.m]](https://github.com/roboticcam/matlab_demos/blob/master/gmm_demo.m)** and **[[kmeans_demo.m]](https://github.com/roboticcam/matlab_demos/blob/master/kmeans_demo.m)** and **[[bilibili video]](https://www.bilibili.com/video/av23901379)** 最大期望E-M的收敛证明, E-M到高斯混合模型的例子, **[[gmm_demo.m]](https://github.com/roboticcam/matlab_demos/blob/master/gmm_demo.m)** 和 **[[kmeans_demo.m]](https://github.com/roboticcam/matlab_demos/blob/master/kmeans_demo.m)** 和 **[[B站视频链接]](https://www.bilibili.com/video/av23901379)** * ### [Markov Chain Monte Carlo](https://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_mcmc.pdf) ### MCMC background, including random matrix, power method convergence, detailed balance and PageRank algorithm, some basic MCMC methods, including Metropolitan-Hasting, Gibbs, and LDA as an example MCMC背景，包括随机矩阵、幂法收敛、详细平衡和PageRank算法，一些基本的MCMC方法，包括Metropolitan-Hasting、Gibbs和LDA为例 * ### [Variational Inference](https://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_vb.pdf) ### Explain Variational Bayes both the non-exponential and exponential family distribution **[[vb_normal_gamma.m]](https://github.com/roboticcam/matlab_demos/blob/master/vb_normal_gamma.m)** and **[[bilibili video]](https://www.bilibili.com/video/av24062247)** 解释变分贝叶斯非指数和指数族分布。**[[vb_normal_gamma.m]](https://github.com/roboticcam/matlab_demos/blob/master/vb_normal_gamma.m)** 和 **[[B站视频链接]](https://www.bilibili.com/video/av24062247)** * ### [State Space Model (Dynamic model)](https://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_ssm.pdf) ### explain in detail of