Linear Algebra for Data Science Flashcards
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Linear Algebra for Data Science Flashcards

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Questions and Answers

Which of the following are valid linear algebra expressions? (Select all that apply)

  • A + B
  • (ABC)^{-1} (correct)
  • CBx
  • A + BC (correct)
  • (AB)^{-1} (correct)
  • Ax + Cx
  • What is the angle between the two vectors a = [1 -2 -1] and b = [-1 1 1]? (Select one)

  • 90 degrees (correct)
  • 45 degrees
  • 0 degrees
  • 60 degrees
  • What is the formula to find the angle θ between two vectors u and v?

    θ = arccos( [u * v] / [||u|| * ||v||])

    Which of the following products are valid given the matrices A, B, and C with dimensions 3x5, 4x3, and 5x8 respectively? (Select all that apply)

    <p>A * C</p> Signup and view all the answers

    Which of the following statements are generally true about transposition? (Select all that apply)

    <p>(A * B)^T = B^T * A^T</p> Signup and view all the answers

    What is the rank of the matrix representing the DataFrame given 5 duplicate rows and 5 duplicate feature vectors?

    <p>n - 5</p> Signup and view all the answers

    What is the max rank of a r x c matrix if r > c?

    <p>c</p> Signup and view all the answers

    What is the max rank of a r x c matrix if r < c?

    <p>r</p> Signup and view all the answers

    What questions can be answered using linear algebra?

    <p>Solving large systems of linear equations, computer vision, machine learning (support vector machines, principal component analysis), optimization techniques, fitting an arbitrary polynomial.</p> Signup and view all the answers

    In a n x d matrix, how many observations and features are there?

    <p>n observations, d features</p> Signup and view all the answers

    Are observations row or column vectors?

    <p>row vectors</p> Signup and view all the answers

    Are features row or column vectors?

    <p>column vectors</p> Signup and view all the answers

    What are the three types of computing?

    <p>CPU -&gt; GPU -&gt; TPU (tensor processing units)</p> Signup and view all the answers

    What is the dot product of the two vectors v1 = (x1, y1) and v2 = (x2, y2)?

    <p>x1x2 + y1y2</p> Signup and view all the answers

    Two vectors do not have to be the same size when taking the dot product.

    <p>False</p> Signup and view all the answers

    What does cos(θ) equal in a perfect correlation (dependent)?

    <p>cos(θ) = 1, θ = 0</p> Signup and view all the answers

    What does cos(θ) equal when there is no correlation (independent)?

    <p>cos(θ) = 0, θ = 90 degrees</p> Signup and view all the answers

    What is the product of the following matrix vector multiplication | a b c | | x | | d e f | | y | | g h i | | z |?

    <p>| ax + by + cz | | dx + ey + fz | | gx + hy + iz |</p> Signup and view all the answers

    What happens geometrically when you multiply a vector by a matrix?

    <ol> <li>Rotation: vector rotates around the origin (clockwise or counterclockwise). 2) Scaling: vector gets scaled &amp; length changes.</li> </ol> Signup and view all the answers

    What happens geometrically when you multiply a matrix by a matrix?

    <p>Performs many rotations and many scalings.</p> Signup and view all the answers

    What does changing the alpha value affect?

    <p>transparency (0 for transparent, 1 for solid)</p> Signup and view all the answers

    How do you take the transpose of a matrix?

    <p>Turn the rows into columns, and columns into rows.</p> Signup and view all the answers

    What is A * inv(A) equal if A is a matrix?

    <p>identity matrix I</p> Signup and view all the answers

    What is the only type of matrix that has an inverse?

    <p>square matrices (n x n)</p> Signup and view all the answers

    What are vector spaces?

    <p>A collection of vectors with operations such as addition, subtraction, and properties such as commutativity and associativity.</p> Signup and view all the answers

    What is a basis for a vector space?

    <p>A subset of the vector space where any vector in the vector space can be spanned by a linear combination of the vectors of the basis.</p> Signup and view all the answers

    Are these vectors linear independent? v = [1 2 3], u = [0 1 2], w = [2 5 8]

    <p>False</p> Signup and view all the answers

    Study Notes

    Valid Linear Algebra Expressions

    • Matrix A is nxn, B is nxm, C is mxn with m > n, and x is a vector nx1.
    • Valid expressions include A+BC, (AB)⁻¹, and (ABC)⁻¹.

    Angle Between Vectors

    • Vectors a = [1, -2, -1] and b = [-1, 1, 1] are orthogonal.
    • The angle between these vectors is 90 degrees.

    Formula for Angle Between Vectors

    • Angle θ between vectors u and v can be calculated using: θ = arccos( [u * v] / [||u|| * ||v||]).

    Valid Matrix Products

    • Given matrices A (3x5), B (4x3), and C (5x8), valid product combinations include:
      • B*A,
      • BAC,
      • A*C.

    Properties of Transpose

    • A^T = A indicates symmetric matrices.
    • (A*B)⁻¹ = B⁻¹ * A⁻¹; transpose of a product reverses the order.
    • (A^T)⁻¹ = A; double transposition returns the original matrix.

    Rank of a DataFrame

    • In a DataFrame with n rows and m columns (m > n), with 5 duplicates, the rank is n - 5.

    Max Rank of Matrices

    • For a r x c matrix:
      • If r > c, max rank is c.
      • If r < c, max rank is r.

    Applications of Linear Algebra

    • Useful for solving large systems of equations, in computer vision, machine learning (like SVMs and PCA), and optimization techniques.

    Observations and Features

    • In a n x d matrix, there are n observations and d features.
    • Observations are organized as row vectors.
    • Features are represented as column vectors.

    Types of Computing

    • The progression in computing types: CPU (central processing unit), GPU (graphics processing unit), TPU (tensor processing unit).

    Dot Product Calculation

    • The dot product of vectors v1 = (x1, y1) and v2 = (x2, y2) is calculated as x1x2 + y1y2.

    Dot Product Size Requirement

    • Two vectors must be the same size to compute their dot product; otherwise, the statement is false.

    Correlation and Cosine

    • Perfect correlation: cos(θ) = 1, leading to θ = 0 degrees.
    • No correlation: cos(θ) = 0, resulting in θ = 90 degrees.

    Matrix-Vector Multiplication

    • The product of a matrix and a vector retains the structure of the result, computed as individual sums of products for each row.

    Geometric Interpretation of Vector-Matrix Multiplication

    • Multiplying a vector by a matrix can result in rotation (around the origin) and scaling (changing vector length).

    Geometric Interpretation of Matrix-Matrix Multiplication

    • Multiplying matrices combines multiple rotations and scalings based on their compositions.

    Alpha Value in Graphics

    • Adjusting the alpha value changes transparency: 0 indicates full transparency, 1 indicates fully opaque.

    Transposing a Matrix

    • Transposition involves converting rows into columns and columns into rows.

    Inverse of a Matrix

    • The product of a matrix A and its inverse inv(A) equals the identity matrix I.

    Type of Matrix with Inverse

    • Only square matrices (n x n) possess an inverse.

    Vector Spaces

    • Vector spaces consist of collections of vectors capable of operations like addition and subtraction, adhering to properties like commutativity and associativity.

    Basis of a Vector Space

    • A basis is a subset of a vector space such that any vector can be expressed as a linear combination of these basis vectors.

    Linear Independence of Vectors

    • Vectors v = [1, 2, 3], u = [0, 1, 2], and w = [2, 5, 8] are not linearly independent, as w can be expressed as a combination of v and u.

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    Test your understanding of linear algebra concepts crucial for data science with these flashcards. Each card presents a question that requires you to think critically about matrix operations and vector calculations. Perfect for students looking to reinforce their knowledge in a practical way.

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