I have trouble understanding the proof which computes hessian of J to see if the optimisation problem is convex. why is the least square cost function for linear regression convex
The proof claims that the matrix $𝑋^T 𝑋$ is positive semidefinite. It's obvious that the product is symmetric. But I am not able to see why it is positive semidefinite.