You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.
This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.
What you will learn
- Familiarity with Equations, Functions, and Graphs
- Differentiation and Optimization
- Vectors and Matrices
- Statistics and Probability
Course Syllabus
- Introduction
- Equations, Functions, and Graphs
- Differentiation and Optimization
- Vectors and Matrices
- Statistics and Probability
Note: This syllabus is preliminary and subject to change.