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

L.P. All rights reserved.© 2024 Bloomberg Finance L.P. All rights reserved. Designing a Slimmer Vector of Variants CppCon 2024 September 18, 2024 Chris Fretz Senior C++ Engineer 2© 2024 Bloomberg details my experience in creating a novel solution to an observed problem with memory usage of std::vector> • The talk starts with the motivating use case, considers several candidate Heterogeneous containers like std::vector> are an extremely natural way to represent data across many different paradigms • Naive usage of std::vector> can result in

1#include #include #include <vector> int a[] = {0, 1, 2, 3, 4}; std::vector c = {0, 1, 2, 3, 4}; auto main() -> int { std::cout << "C style array: " << sizeof(a) / sizeof(a[0]) sizeof(a[0]) << std::endl; std::cout << "Vector size: " << c.size() << std::endl; return 0; } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 https://www.reddit.com/r/cpp/comments/qm6i25/my_professor_is_telling _us_to_never_use_vectors/ 2more std::vector? 3How do you read a technical book front to back? As a technical editor... 4#include int main() { int cppconEarlyYears[6] = {2014, 2015, 2016

Back to Basics: The Abstract Machine Bob Steagall CppCon 2020 K E W B C O M P U T I N GCopyright © 2020 Bob Steagall K E W B C O M P U T I N G Overview/Goals • Describe abstract machines in general general • Describe the C++ abstract machine specifically • Language goals that drive its design • Role in program development and execution • Important definitions and characteristics • Important components components of the abstract machine, and their relationships • Provide a useful overview of the C++ abstract machine CppCon 2020 - The Abstract Machine 2Copyright © 2020 Bob Steagall K E W B C O M P U T I

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

Debugging the BPF Virtual Machine Lorenzo Fontana October 28, 2020 ● Debugging is useful to understand how things work ● Sometimes, eBPF programs can’t even load ● I couldn’t find good resources on this this, so, here I am ● I break lots of eBPF programs ● The BPF Virtual machine is not easy to understand Why ? The BPF subsystem lives in the kernel AND The kernel can be debugged using gdb The

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page in ClickHouse › stochasticLinearRegression › stochasticLogisticRegression Stochastic methods do support multiple factors. That’s not the most important difference. 23 / 62 Stochastic linear regression CatBoost advantages › Good quality for default parameters › Sophisticated categorical features support › Models analysis tools 44 / 62 Gradient Boosting 45 / 62 Gradient Boosting 46 / 62 Gradient

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page in ClickHouse › stochasticLinearRegression › stochasticLogisticRegression Stochastic methods do support multiple factors. That’s not the most important difference. 23 / 62 Stochastic linear regression CatBoost advantages › Good quality for default parameters › Sophisticated categorical features support › Models analysis tools 44 / 62 Gradient Boosting 45 / 62 Gradient Boosting 46 / 62 Gradient