The primary goal of this text is to understand and apply the mathematics of linear algebra and optimization to develop machine learning and data analysis, which will form the focus of the second half of the text. Machine learning refers to a class of algorithms that learn to complete tasks, such as image classification, face recognition, text generation, etc., from examples or experience, and are not explicitly programmed with a list of instructions to follow. For example, to perform handwritten digit recognition with a machine learning algorithm, one would provide many examples (sometimes hundreds or thousands) of images of handwritten digits and their known labels, and the algorithm will attempt to learn a general rule that is able to to correctly label new instances.