The objective of chapter 7 is to provide explicit conditions for ensuring the convergence of a large class of commonly used batch learning algorithms. The  Zoutendijk-Wolfe convergence theorem is introduced as a tool for investigating the asymptotic behavior of batch learning algorithms. The change in parameter estimates at each iteration is defined as a stepsize multiplied by a search direction vector.  The search direction must satisfy a downhill condition which requires that the search direction vector is less than ninety degrees from the negative gradient descent direction. Rather than choosing a computationally expensive optimal stepsize, the concept of a Wolfe stepsize is introduced that includes an optimal stepsize as a special case but allows for sloppy stepsize choices. The Zoutendijk-Wolfe convergence theorem provides sufficient conditions to ensure a batch learning algorithm with Wolfe stepsize and downhill search direction will converge to a set of critical points of a possibly nonconvex objective function.  Convergence of commonly used algorithms used to estimate parameters is analyzed using the Zoutendijk-Wolfe convergence theorem. These algorithms include: linear regression, logistic regression, multilayer perceptrons, gradient descent, Newton-Raphson, Levenberg-Marquardt, limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), conjugate gradient, block coordinate gradient descent, natural gradient descent, and variants of RMSPROP.