The course has been designed to help breakdown these mathematical concepts and ideas by dividing the syllabus into three main sections which include:
Linear Algebra - Throughout the field of Machine Learning, linear algebra notation is used to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating.
Multivariate Calculus - This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance.
Probability Theory - The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course.