Duality is a principle in mathematical optimization theory which states that optimization problems can be viewed from two perspectives, the primal and dual problem. Weak duality states that any feasible solution to the primal problem is at least as large as any feasible solution to the dual problem. Strong duality states that for convex optimization problems, the optimal values of the primal and dual problems are equal.

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.