# Mathematical Foundation For Machine Learning and AI

## Description

Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with the self-driving cars, medical diagnosis and even betting humans at strategy games like Go and Chess.

The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge.

Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.

The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory.

Linear Algebra – Linear algebra notation is used in Machine Learning 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.

It covers topics such as:

• Scalars, Vectors, Matrices, Tensors
• Matrix Norms
• Special Matrices and Vectors
• Eigenvalues and Eigenvectors

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.

It covers topics such as:

• Derivatives
• Integrals
• Differential Operators
• Convex Optimization

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.

It covers topics such as:

• Elements of Probability
• Random Variables
• Distributions
• Variance and Expectation
• Special Random Variables

The course also includes projects and quizzes after each section to help solidify your knowledge of the topic as well as learn exactly how to use the concepts in real life.

At the end of this course, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects.