Here are a few further suggestions:

(1) The intersection of planes and lines (relevant in computer graphics) leads to linear equations and thus to kernels of linear maps.

(2) The area of parallelograms (and thus of triangles) can be computed in terms of determinants. That's maybe a bit too simple because you only need very small matrices, but on the other hand many surfaces in computer graphics are constructed from triangles.

If you compute the area of triangles in three dimensional space you'll also need to multiply $3\times 2$-matrices with their tranposed matrices.

(3) A simple way to represent directed graphs on a computer is to store their adjacency matriy $A$ as a double array. Transposing the matrix means inverting the direction of all edges.

Moreover, the entries of the $k$-th power $A^k$ tell you how many paths of length $k$ exist between any two vertices of the graph.

(4) The pseudo-inverse of a matrix (which is closely related to singular value decomposition) is very important for linear regression/least square fitting.

(5) Consider a least square problem where you have, in addition, a linear restriction on the fitting parameters (probably, it would be helpful to present this situation in terms of a more concrete example). This means you restrict your parameter space to the kernel of a linear map. Now, use for instance the Gauss algorithm to represent your kernel as the image of another linear map. This yields a coordinate transform which represents your fitting problem without any restriction - so now you can simply solve it by using the pseudo-inverse approach.

(6) Again an application from computer graphics: have your students build some nice three-dimensional geometric object (for instance, consisting of many triangles) and let them make it "visible" by projecting the object to the screen. (This is going to be a nice exercise in computing intersections of lines and planes because they need to determine which triangles are in front of the others).

Now, you want to rotate the object - so your students will need to compute rotation matrices and apply them to the coordinates of the triangles. This is a nice illustration of orthogonal matrices and of the action of matrices as linear maps.

(7) An illustration (though, admittedly not an application) of eigenvalues and eigenspaces: Have your students draw an ellipse with axes parallel to the coordinate axes in $\mathbb{R}^2$, then have them rotate the ellipse by using certain orthogonal matrices.

Afterwards, give them a quadratic equation in two variables, and make them plot the solution set (which should be chosen by you to agree with the rotated ellipse from above). Then have them diagonalize the symmetric matrix representing the corresponding quadratic form and make them compare their result with the rotation matrix above.

(8) Another example from image processing (in addition to James' example of image compression): many image "improvement" techniques are based on applying filters to the image - which is simply an application of a linear map.

(9) The discrete Fourier transform - which can by represented by a unitary matrix - is frequently used in image processing.

(10) In case that you wish to go into numerical analysis: discretization methods for linear partial differential equations usually yield linear equations in finite dimensions which have to be solved numerically.

Here are a few further suggestions:

(1) The intersection of planes and lines (relevant in computer graphics) leads to linear equations and thus to kernels of linear maps.

(2) The area of parallelograms (and thus of triangles) can be computed in terms of determinants. That's maybe a bit too simple because you only need very small matrices, but on the other hand many surfaces in computer graphics are constructed from triangles.

If you compute the area of triangles in three dimensional space you'll also need to multiply $3\times 2$-matrices with their tranposed matrices.

(3) A simple way to represent directed graphs on a computer is to store their adjacency matriy $A$ as a double array. Transposing the matrix means inverting the direction of all edges.

Moreover, the entries of the $k$-th power $A^k$ tell you how many paths of length $k$ exist between any two vertices of the graph.

(4) The pseudo-inverse of a matrix (which is closely related to singular value decomposition) is very important for linear regression/least square fitting.

(5) Consider a least square problem where you have, in addition, a linear restriction on the fitting parameters (probably, it would be helpful to present this situation in terms of a more concrete example). This means you restrict your parameter space to the kernel of a linear map. Now, use for instance the Gauss algorithm to represent your kernel as the image of another linear map. This yields a coordinate transform which represents your fitting problem without any restriction - so now you can simply solve it by using the pseudo-inverse approach.

(6) Again an application from computer graphics: have your students build some nice three-dimensional geometric object (for instance, consisting of many triangles) and let them make it "visible" for instance by a simple raytracing algorithm. (This is going to be a nice exercise in computing intersections of lines and planes).

Now, you want to rotate the object - so your students will need to compute rotation matrices and apply them to the coordinates of the triangles. This is a nice illustration of orthogonal matrices and of the action of matrices as linear maps.

(7) An illustration (though, admittedly not an application) of eigenvalues and eigenspaces: Have your students draw an ellipse with axes parallel to the coordinate axes in $\mathbb{R}^2$, then have them rotate the ellipse by using certain orthogonal matrices.

Afterwards, give them a quadratic equation in two variables, and make them plot the solution set (which should be chosen by you to agree with the rotated ellipse from above). Then have them diagonalize the symmetric matrix representing the corresponding quadratic form and make them compare their result with the rotation matrix above.

(8) Another example from image processing (in addition to James' example of image compression): many image "improvement" techniques are based on applying filters to the image - which is simply an application of a linear map.

(9) The discrete Fourier transform - which can by represented by a unitary matrix - is frequently used in image processing.

(10) In case that you wish to go into numerical analysis: discretization methods for linear partial differential equations usually yield linear equations in finite dimensions which have to be solved numerically.

Here are a few further suggestions:

(1) The intersection of planes and lines (relevant in computer graphics) leads to linear equations and thus to kernels of linear maps.

(2) The area of parallelograms (and thus of triangles) can be computed in terms of determinants. That's maybe a bit too simple because you only need very small matrices, but on the other hand many surfaces in computer graphics are constructed from triangles.

If you compute the area of triangles in three dimensional space you'll also need to multiply $3\times 2$-matrices with their tranposed matrices.

(3) A simple way to represent directed graphs on a computer is to store their adjacency matriy $A$ as a double array. Transposing the matrix means inverting the direction of all edges.

Moreover, the entries of the $k$-th power $A^k$ tell you how many paths of length $k$ exist between any two vertices of the graph.

(4) The pseudo-inverse of a matrix (which is closely related to singular value decomposition) is very important for linear regression/least square fitting.

(5) Consider a least square problem where you have, in addition, a linear restriction on the fitting parameters (probably, it would be helpful to present this situation in terms of a more concrete example). This means you restrict your parameter space to the kernel of a linear map. Now, use for instance the Gauss algorithm to represent your kernel as the image of another linear map. This yields a coordinate transform which represents your fitting problem without any restriction - so now you can simply solve it by using the pseudo-inverse approach.

(6) Again an application from computer graphics: have your students build some nice three-dimensional geometric object (for instance, consisting of many triangles) and let them make it "visible" by projecting the object to the screen. (This is going to be a nice exercise in computing intersections of lines and planes because they need to determine which triangles are in front of the others).

Now, you want to rotate the object - so your students will need to compute rotation matrices and apply them to the coordinates of the triangles. This is a nice illustration of orthogonal matrices and of the action of matrices as linear maps.

(7) An illustration (though, admittedly not an application) of eigenvalues and eigenspaces: Have your students draw an ellipse with axes parallel to the coordinate axes in $\mathbb{R}^2$, then have them rotate the ellipse by using certain orthogonal matrices.

Afterwards, give them a quadratic equation in two variables, and make them plot the solution set. Then have them diagonalize the symmetric matrix representing the corresponding quadratic form and make them compare their result with the rotation matrix above.

(8) Another example from image processing (in addition to James' example of image compression): many image "improvement" techniques are based on applying filters to the image - which is simply an application of a linear map.

(9) The discrete Fourier transform - which can by represented by a unitary matrix - is frequently used in image processing.

(10) In case that you wish to go into numerical analysis: discretization methods for linear partial differential equations usually yield linear equations in finite dimensions which have to be solved numerically.

Here are a few further suggestions:

(1) The intersection of planes and lines (relevant in computer graphics) leads to linear equations and thus to kernels of linear maps.

(2) The area of parallelograms (and thus of triangles) can be computed in terms of determinants. That's maybe a bit too simple because you only need very small matrices, but on the other hand many surfaces in computer graphics are constructed from triangles.

If you compute the area of triangles in three dimensional space you'll also need to multiply $3\times 2$-matrices with their tranposed matrices.

(3) A simple way to represent directed graphs on a computer is to store their adjacency matriy $A$ as a double array. Transposing the matrix means inverting the direction of all edges.

Moreover, the entries of the $k$-th power $A^k$ tell you how many paths of length $k$ exist between any two vertices of the graph.

(4) The pseudo-inverse of a matrix (which is closely related to singular value decomposition) is very important for linear regression/least square fitting.

(5) Consider a least square problem where you have, in addition, a linear restriction on the fitting parameters (probably, it would be helpful to present this situation in terms of a more concrete example). This means you restrict your parameter space to the kernel of a linear map. Now, use for instance the Gauss algorithm to represent your kernel as the image of another linear map. This yields a coordinate transform which represents your fitting problem without any restriction - so now you can simply solve it by using the pseudo-inverse approach.

(6) Again an application from computer graphics: have your students build some nice three-dimensional geometric object (for instance, consisting of many triangles) and let them make it "visible" by projecting the object to the screen. (This is going to be a nice exercise in computing intersections of lines and planes because they need to determine which triangles are in front of the others).

Now, you want to rotate the object - so your students will need to compute rotation matrices and apply them to the coordinates of the triangles. This is a nice illustration of orthogonal matrices and of the action of matrices as linear maps.

(7) An illustration (though, admittedly not an application) of eigenvalues and eigenspaces: Have your students draw an ellipse with axes parallel to the coordinate axes in $\mathbb{R}^2$, then have them rotate the ellipse by using certain orthogonal matrices.

Afterwards, give them a quadratic equation in two variables, and make them plot the solution set (which should be chosen by you to agree with the rotated ellipse from above). Then have them diagonalize the symmetric matrix representing the corresponding quadratic form and make them compare their result with the rotation matrix above.

(8) Another example from image processing (in addition to James' example of image compression): many image "improvement" techniques are based on applying filters to the image - which is simply an application of a linear map.

(9) The discrete Fourier transform - which can by represented by a unitary matrix - is frequently used in image processing.

(10) In case that you wish to go into numerical analysis: discretization methods for linear partial differential equations usually yield linear equations in finite dimensions which have to be solved numerically.