You will learn to use descriptive statistics and visualization techniques to explore datasets, solve linear equations, and model relationships using linear regression. The course covers foundational principles of probability, including Bayes' Theorem and advances into calculus, focusing on derivatives and integrals to analyze rates of change and distributions crucial for optimization and modeling in AI. This course is designed to provide the mathematical fluency necessary for more advanced stuff
Learning Goals:
- -Essential Functions with Description and Visualization of Data
- -Vectors, Matrices and Linear Equations
- -Linear Transformations and Vector Geometry
- -Determinants and Eigenvectors
- -Probability Distributions
- -Derivatives and Rates of Change
- -Optimization
- -Integration and Probability
- -Partial Derivatives and Gradients
Coursera - Reference Course
Our course is based on the "Foundational Mathematics for AI" course offered by Johns Hopkins University on Coursera, which equips students with essential mathematical skills for advanced AI studies and research.
Check it out:
https://www.coursera.org/learn/foundational-mathematics-for-ai
another alternative is:
https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science
If you prefer some free alternatives - check out the YouTube-Playlists
YouTube-Playlists:
Essence of Linear Algebra:
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
Essence of Calculus:
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
Math for Deep Learning
https://www.youtube.com/playlist?list=PL05umP7R6ij0bo4UtMdzEJ6TiLOqj4ZCm
Mathematics for Machine Learning:
https://www.youtube.com/playlist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS