Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn Rn is the outward pointing normal vector, and b is the bias term. The n-dimensional space is separated into two half-spaces H+ = {x ∈ Rn | ωT x + b ≥ 0} and H− = {x ∈ Rn | ωT x + b < 0} by the hyperplane margin is defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review Hyperplane Separates a n-dimensional space into two half-spaces Defined by an outward pointing normal vector ω ∈ Rn Assumption: The hyperplane passes through origin. If not, have a bias term b; we will then along ω (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx +

Machine Learning Lecture 10: Neural Networks and Deep Learning Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 Deep Feedforward f(x) is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practioners • The conventional neural networks (CNN) used for object recognition from photos

Machine Learning Pytorch Tutorial TA : 曾元（Yuan Tseng） 2022.02.18 Outline ● Background: Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors year ■ ref: link1, link2 Some knowledge of NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) translation, synthesis, ...) ○ Most implementations of recent deep learning papers ○ ... References ● Machine Learning 2021 Spring Pytorch Tutorial ● Official Pytorch Tutorials ● https://numpy.org/ Any questions

#IstioCon Is Your Virtual Machine Really Ready-to-go with Istio? Kailun Qin, Intel Haoyuan Ge #IstioCon Quick Summary (from Google Cloud Next ’19 [1]) VM works on Istio! [1] Istio Service Mesh Observability ○ See VM metrics alongside containers ● Extensibility #IstioCon Why Should Istio Support VMs ● ≈ Why VMs? ○ Technical reasons ■ Better known security controls ■ Better isolation (of Multi Clouds #IstioCon Istio VM Integration is? A Tumultuous Odyssey… [1] Istio 1.8: A Virtual Machine Integration Odyssey, Jimmy Song #IstioCon V0.2 Mesh Expansion ● Prerequisites ○ IP connectivity

A Day in the Life of a Data Scientist Conquer Machine Learning Lifecycle on Kubernetes Brian Redmond • Cloud Architect @ Microsoft (18 years) • Azure Global Black Belt Team • Live in Pittsburgh, PA idle • Parallel training instead of sequential: huge time saver for large trainings Kubeflow • Machine Learning Toolkit for Kubernetes • To make ML workflows on Kubernetes simple, portable, and scalable

inputs have similar representations. We will call this representation an Embedding. An embedding is a vector of features that represent aspects of an input numerically. It must fulfill the following goals: such as text, image, audio, video, etc. to a low-dimensional representation such as a fixed length vector of floating point numbers, thus performing dimensionality reduction1. b) The low-dimensional representation examples / more than two features? In those cases, we could use classical machine learning algorithms like the Support Vector Machine4 (SVM) to learn classifiers that would do this for us. We could rely on

01 支持向量机概述 02 线性可分支持向量机 03 线性支持向量机 04 线性不可分支持向量机 4 1.支持向量机概述 支 持 向 量 机 （ Support Vector Machine, SVM ） 是 一 类 按 监 督 学 习 （ supervised learning）方式对数据进行二元分类的广义线性 分类器（generalized linear classifier），其决 况。软间隔，就是允许一定量的样本分类错误。 软间隔 硬间隔 线性可分 线性不可分 6 支持向量 1.支持向量机概述 算法思想 找到集合边缘上的若干数据（称为 支持向量（Support Vector）） ，用这些点找出一个平面（称为决 策面），使得支持向量到该平面的 距离最大。 距离 7 1.支持向量机概述 背景知识 任意超平面可以用下面这个线性方程来描述： ?T 辑回归 或不带核函数的支持向量机。 28 参考文献 [1] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273–297. [2] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www

start off on our journey to more efficient deep learning models. Introduction to Deep Learning Machine learning is being used in countless applications today. It is a natural fit in domains where there problems where we expect exact optimal answers, machine learning applications can often tolerate approximate responses, since often there are no exact answers. Machine learning algorithms help build models, which Relation between Artificial Intelligence, Machine Learning, and Deep Learning. Deep learning is one possible way of solving machine learning problems. Machine learning in turn is one approach towards

takes a 32-bit floating point value in the range [-10.0, 10.0]. We need to transmit a collection (vector) of these variables over an expensive communication channel. Can we use quantization to reduce transmission learnings from the previous exercise into practice. We will code a method `quantize` that quantizes a vector x, given xmin, xmax, and b. It should return the quantized values for a given x. Logistics We just state that in this book, we have chosen to work with Tensorflow 2.0 (TF) because it has exhaustive support for building and deploying efficient models on devices ranging from TPUs to edge devices at the time