This lecture explains the key hyperparameters in Support Vector Machines (SVM), focusing on the C parameter for controlling misclassifications and the kernel width parameter for adjusting the smoothness of the boundary. The instructor illustrates how different values of these hyperparameters impact the classification results, highlighting the trade-off between accuracy and overfitting.