Explore the fundamental algorithm driving machine learning in this 40-minute video lecture. Delve into the concept of backpropagation, deriving it from first principles. Begin with a historical background before tackling the curve fitting problem. Compare random and guided adjustments, then progress to derivatives and gradient descent. Examine higher dimensions and gain intuition on the chain rule. Investigate computational graphs and automatic differentiation. Conclude with a comprehensive summary and access additional resources, including Andrej Karpathy's playlist and Jürgen Schmidhuber's blog on backpropagation history. Enhance your understanding of this crucial machine learning concept through clear explanations and practical examples.