• Linear Regression

    PREVIOUS: Introduction Linear Regression For linear regression, we want to find Y = WX + b that gives us the lowest L value. Y is our Model W is a matrix of weights X is a matrix of our inputs B is our bias L = (Y - y)^2, with...

  • Introduction

    Machine Learning in the context of AI Often times, these two phrases are used interchangeably. Rather, we should see machine learning as one of the tools that is used in the quest to develop artificial intelligence. See more on some of the ideas behind AI here. Say we want to...

  • Intro to Machine Learning

    I’m currently auditing this class at UCB in the summer of 2018. This class is designed so that you can understand what’s under the hood of machine learning. It is not a collection of ML algorithms. It is mandatory that you understand Probability and Statistics Multivariable Calculus Linear Algebra Proofs...

  • ROS

    PREVIOUS: Introduction Disclaimer: I can confidently say that this has the biggest learning curve of anything I’ve learned–and a lot of people have agreed. I don’t say this to scare you away, but you need to take ROS seriously. But don’t be discouraged! Find the help you need, ask questions,...

  • Introduction to Robotics

    PREVIOUS: Table of Contents Think of the Google Car: How does it work? This field of study dives in to a whole realm of visual detection, sensor awareness, ridiculous math, coding, etc. What are robots? Robot: slave work. “A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized...

  • Robotics Table of Contents

    BACK: Classes Preface I took this class in the Spring of 2018, with Stefano Carpin. The course objectives: Learn the math and computational methods necessary to model and solve kinematic problems involving mobile robots familiarize with the most common robot sensors and understand fundamental sensor processing algorithms and their engineering...

  • Lesson Plans (in order of creation)

    BACK: Navigation Introduction to Limits on 04/28/2017 at El Capitan High School in Kyle Clinton’s class for NSED 73/74. Scatter Plots v1 on 04/12/2018 at Rudolph Rivera Middle School in Jennifer Alford’s class for NSED 43/44. Introduction to Derivatives not given, for NSED 100. Group members: Enrique M. and Josue...

  • Maximum Flow

    BACK: Shortest Path Algorithms Suppose I’m Amazon and Prime is stressing me out. I have a bunch of warehouses all over California and I want to ship things from one warehouse (source \(s\)) to another warehouse (sink \(t\)). I have different trucks heading out to different locations and trucks leaving...