Start Date: 02/23/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/pca-machine-learning
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms.
Principal Component Analysis (PCA) is one of the most important dimensionality reduction algorithms in machine learning. In this course, we lay the mathematical foundations to derive and understand PCA from a geometric point of view. In this module, we learn how to summarize datasets (e.g., images) using basic statistics, such as the mean and the variance. We also look at properties of the mean and the variance when we shift or scale the original data set. We will provide mathematical intuition as well as the skills to derive the results. We will also implement our results in code (jupyter notebooks), which will allow us to practice our mathematical understand to compute averages of image data sets.
Mathematics for Machine Learning: PCA and Linear Algebra This course gives you an introduction to the PCA and linear algebra that you need in order to understand the workings of various machine learning algorithms. We will cover both the linear algebra and the mathematical foundations of PCA. We will start by introducing the math behind the most common machine learning algorithms: linear gradient descent, L2-weighted smoothing, and Keras. We will also cover the calculus required to fit these algorithms to datasets. We will also cover the general concepts required to understand the code generated by the algorithms. Finally, we will cover the differences between linear algebra and modern optimization.Mathematics for Machine Learning: PCA L2-Weighted Smoothing Linear Gradient Descent Binomial Coefficients Mathematics for Machine Learning: Differential Equations This course gives you an overview of mathematical topics in machine learning, including differential equations and approximate methods of computing values for variables in more advanced ways. We will start by introducing the mathematics behind the most common machine learning algorithms: linear gradient descent, L2-weighted smoothing, and Keras. We will also cover the calculus required to fit these algorithms to datasets. We will also cover the differences between linear algebra and modern optimization.Mathematics for Machine Learning: Differential Equations L2-Weighted Smoothing Linear Gradient Descent
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Investigations in Mathematics Learning | Investigations in Mathematics Learning is the official research journal of the Research Council for Mathematics Learning. Information about submission can be found here. RCML seeks to stimulate, generate, coordinate, and disseminate research efforts designed to understand and/or influence factors that affect mathematics learning. |
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