你将学到什么
Familiarity with Equations, Functions, and Graphs
Differentiation and Optimization
Vectors and Matrices
Statistics and Probability
课程概况
Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
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.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
课程大纲
Introduction
Equations, Functions, and Graphs
Differentiation and Optimization
Vectors and Matrices
Statistics and Probability
Note: This syllabus is preliminary and subject to change.
预备知识
To complete this course successfully, you should have:
A basic knowledge of math
Some programming experience - R is preferred.
A willingness to learn through self-paced study.
常见问题
Q: The prerequisites include R Programming - why do I need this to learn math?
A: This course is specifically aimed at students who want to apply math to machine learning and artificial intelligence - R is used extensively in these fields. The practical elements of this course involve implementing mathematical techniques in R code. For the most part, you'll be given the code you need to complete the exercises; but a basic knowledge of R syntax will improve your understanding of what's going on in the labs and demonstrations. Consider taking course DAT204x: Introduction to R for Data Science before taking this class.
Q: What hardware and software do I need to complete this class?
A: You will need a computer running Windows, Mac OSX, or Linux, and a web browser. Optionally, you can install Python 3.x - but you will be able to complete the labs using a free online environment, so this is not required.
Q: Will I need a Microsoft Azure subscription to complete this class?
A: No. You will be able to complete the labs using a local Python installation or the Microsoft Azure Notebooks service, which is a free service that does not require an Azure subscription.
