When will I come across Eigenvalue concept Okay, small question . I'm a student in India. I have come across numerous questions regarding Eigenvalues and Eigenvectors on this site. I tried reading the concept off Wikipedia but they didn't made much sense. Can someone suggest me a good reference for this topic?
Also, Anyone who knows about India academic system, can you tell me when do we come across such topics if I pursue Computer Science And Engineering in college?
Thank you. 
 A: I am guessing that the available explanations don't convey any immediate understanding to you. Perhaps a simple visual and practical example will give you an idea of what is going on by using your intuition. Suppose you have a rectangular image or photograph. You would like to resize it to zoom in to examine details, or else to shrink it to fit in a smaller rectangle or square such as an icon or avatar. The conversion process should be linear in order to minimize distortion. So you take the source rectangle and map it to the target rectangle while keeping linear relations. That is, source border edges get mapped to the target border edges linearly. So, the midpoint of edges get mapped to midpoints and similarly for other proportions. What you notice is that each horizontal line gets mapped into a corresponding horizontal line and the line is multiplied by a constant factor which is the same for each horizontal line. This constant factor is called an "eigenvalue". A horizontal line is an "eigenvector" for its corresponding eigenvalue. An exactly similar thing happens for vertical lines. What about a diagonal line? Unless the eigenvalues for horizontal and vertical lines are the same, a diagonal line gets mapped to a line with a different slope. Thus, eigenvectors are special because when mapped their slope doesn't change. If the mapping preserves the aspect ratio, then the two eigenvalues are the same, and in this case, all lines are eigenvectors. This is a common case when you want to just zoom in or out. I hope this makes more sense to you.
