Question 1
Which statement best explains why linear algebra is widely used in machine learning?
A. It provides a compact way to represent data and learn from patterns using vectors and matrices. B. It eliminates the need for data by replacing measurement with pure logic. C. It only works for one-variable problems with no interactions. D. It replaces every other branch of mathematics in modern computing.
Question 2
In many applications, what does a matrix most commonly represent?
A. A rectangular table of numbers that organizes data or coefficients. B. A single number used to label a problem. C. A geometric shape with no numerical meaning. D. A random list of words with no structure.
Question 3
What does it mean for a transformation to be linear?
A. It preserves addition and scalar multiplication. B. It always sends every vector to the zero vector. C. It must be represented by a triangle. D. It only works when the input has exactly two entries.
Question 4
In image processing, why can an image be treated as a matrix?
A. Because each pixel value can be placed in rows and columns. B. Because images have no numerical information. C. Because every image is always a straight line. D. Because matrices can only store one color and nothing else.
Question 5
What does the dimension of a vector space tell us?
A. The number of vectors in any basis for the space. B. The total number of vectors that can ever exist in the space. C. The sum of all entries in one special vector. D. The number of equations in every system of linear equations.
