Welcome to module 2

Course video 9 of 32

Data can be interpreted as vectors. Vectors allow us to talk about geometric concepts, such as lengths, distances and angles to characterise similarity between vectors. This will become important later in the course when we discuss PCA. In this module, we will introduce and practice the concept of an inner product. Inner products allow us to talk about geometric concepts in vector spaces. More specifically, we will start with the dot product (which we may still know from school) as a special case of an inner product, and then move toward a more general concept of an inner product, which play an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes). We have a lot of exercises in this module to practice and understand the concept of inner products.

Sobre o Coursera

Cursos, especializações e graduações on-line, ministradas pelos melhores instrutores das melhores universidades e instituições de ensino.

Join a community of 40 million learners from around the world
Earn a skill-based course certificate to apply your knowledge
Gain confidence in your skills and further your career