Continuous Mathematical Methods with an Emphasis on Machine Learning
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 be on machine learning’s underlying mathematical methods, including computational linear algebra and optimization. Special topics will include automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, and RNNs.
Topics Include
- Computational linear algebra and optimization
- Automatic differentiation via backward propagation
- Momentum methods from ordinary differential equations
- Conjugate gradient method
- Ordinary and partial differential equations
- Vector and tensor calculus
- Convolutional neural networks
Course Page
Price
$4,056.00
Subject to change
Delivery
Online, instructor-led
Level
Introductory
Commitment
10 weeks, 10-20 hrs/week
School
Stanford School of Engineering
Language
English