The Mathematics of Artificial Intelligence
Dany Entezari
Mathematician, Programmer | Bignumber
The Mathematics of AI — from First Principles.
AI is incredibly fascinating, but few understand the how it all really works. That's because few actually study the mathematics of AI.
This course is designed to teach you artificial intelligence from a mathematical perspective. You will learn how AI actually works and master powerful mathematical techniques along the way, from Probability Theory to Linear Algebra and Calculus.
So how is this course different?
This course teaches the mathematics of AI—with the prerequisites included. It is designed to be self-contained.
Almost everyone who struggles with mathematics struggles because they don't know how different branches of mathematics are related or how to bridge the information gap.
This course covers the mathematics needed to understand topics likes transformers, neural networks, and embeddings.
This course is different because it does exactly that. This is how I learned mathematics myself! This is how I've taught students and professionals who later pursued degrees and roles in AI and, generally, data science.
Those who learn any subject from first principles will be the first to master the more advanced parts of it. They will always be more successful in their discipline.
What you’ll learn
A first principles course on the mathematics of machine learning and the implementation of models at the computer system level.
How models see information as probability distributions
How Bayesian Probability helps models to make inferences
How mixture models help models understand more complex information
Powerful techniques for extracting relations and controlling dimensionality
Vectors, matrices, and operations for neural networks, transformers, and large language models
The architecture and operations of transformers that have made LLMs like ChatGPT and Grok possible
How differential calculus is used for training and optimizing models
How neural networks use matrices and calculus for encoding and decoding information
How feedforward and back-propagation work in different types of neural network architectures
Use programming as a bridge to learn mathematical techniques more easily
Use your mathematical ability to better understand machine learn code
Use your programming skills to implement the mathematics of AI for real-world applications of AI
Learn directly from Dany
Dany Entezari
Mathematician | Founder at Bignumber (Computational Research)
Who this course is for
Professionals and students seeking to develop more advanced mathematical skills for artificial intelligence and machine learning.
What's included
Live sessions
Learn directly from Dany Entezari in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
3 live sessions • 16 lessons • 4 projects
Week 1
Feb
3
Models, Predictions, and Embeddings
Tue 2/37:30 PM—9:00 PM (UTC)Feb
5
Introduction to Neural Networks
Thu 2/57:30 PM—9:00 PM (UTC)Feb
7
Training, Evaluating, and Metrics
Sat 2/77:30 PM—9:00 PM (UTC)
Foundations
Week 2
Probability Theory and Linear Algebra for Machine Learning
Free resources
Schedule
Live sessions
8-10 hrs / week
Tue, Feb 3
7:30 PM—9:00 PM (UTC)
Thu, Feb 5
7:30 PM—9:00 PM (UTC)
Sat, Feb 7
7:30 PM—9:00 PM (UTC)
Capstone Projects
6-8 hrs / week
Async content
2-4 hrs / week
Testimonials
From Dany's Data Science Bootcamp: It was a thrilling rollercoaster learning curve at the intensive Data Science Bootcamp. Thanks, instructor Dany Entezari for demystifying Data Science and Machine Learning with unforgettable analogies.
Heba Bayrakdar
Data Scientist, Renewable Energy, EnerwhereFrom Dany's Data Science Bootcamp: A milestone indeed! Thank you Dany Entezari for the intensive content and support all the way to make us achieve. It was an exhaustive and yet foundational approach towards Data Science.
Neha Singh
Data Scientist, Healthcare, M42 HealthFrom Dany's Data Science Bootcamp: Thank you Dany for your patience and giving us the needed educational tools to learn everything in our bootcamp!
Charles Sampedro
Bids and Proposals Management, Noble Corporation
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