Aug 29, 2025 · Math provides the theoretical foundation for understanding how machine learning algorithms work. Concepts like calculus and linear algebra enable fine-tuning of models for …

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Learn about the prerequisite mathematics for applications in data science and machine learning.

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments.

Mathematics for Machine Learning and Data Science is a beginner-friendly specialization where you’ll master the fundamental mathematics toolkit of machine learning: calculus, linear …

This course aims to bridge the gap between a thorough knowledge of mathematics and the machine learning methods that are based on it.

Many of you want to work in machine learning, but the maths skills needed may seem overwhelming. I am here to tell you that it’s nowhere as intimidating as you may think and to …

In this course, you’ll survey numerical approaches to the continuous mathematics used in computer vision and robotics—with an emphasis on machine and deep learning. Our focus will …

We focus on applied math concepts tailored specifically for machine learning — linear algebra, calculus, probability, and optimization — all explained in context with real ML models and …

Probability and statistics are central to the design and analysis of ML algorithms. This note introduces some of the key concepts from probability useful in understanding ML. There are …