Continuous Mathematical Methods with an Emphasis on Machine Learning
CS205L
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
What You Need to Succeed
- A conferred bachelor’s degree with an undergraduate GPA of 3.0 or better
- Linear Algebra and Multivariable Calculus (MATH51), Matrix Theory (MATH104 or MATH113), or equivalents or comfort with the associated material
What You Need To Get Started
Before enrolling in your first graduate course, you must complete an online application.
Don’t wait! While you can only enroll in courses during open enrollment periods, you can complete your online application at any time.
Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your mystanfordconnection account at any time.
Learn more about the graduate application process.
