Kilian Weinberger is a professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in machine learning under the supervision of Lawrence Saul, and his undergraduate degree in mathematics and computing from the University of Oxford. In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He is the recipient of the Daniel M. Lazar ’29 Excellence in Teaching Award (2016) and the Ann S. Bowers Teaching and Advising Excellence Award (2024). As of 2024, he is an ACM and AAAI fellow and in 2021 became a Blavatnik National Awards Finalist. Since 2024 he has been a member of the Sloan Research Fellowships Selection Committee. Weinberger’s research focuses on machine learning and its applications. In particular, he has worked on learning under resource constraints, metric learning, AI in science, computer vision, autonomous vehicles, Gaussian processes, and deep learning. Before joining Cornell University, he was an associate professor at Washington University in St. Louis, and before that, he worked as a research scientist at Yahoo! Research in Santa Clara.
Course Overview
In this course, you will explore support-vector machines and use them to find a maximum margin classifier. You will then construct a mental model for how loss functions and regularizers are used to minimize risk and improve generalization of a learning model. Through the use of feature expansion, you will extend the capabilities of linear classifiers to find non-linear classification boundaries. Finally, you will employ kernel machines to train algorithms that can learn in infinite dimensional feature spaces.
These courses are required to be completed prior to starting this course:
- Problem-Solving with Machine Learning
- Estimating Probability Distributions
- Learning with Linear Classifiers
- Decision Trees and Model Selection
- Debugging and Improving Machine Learning Models
Key Course Takeaways
- Find a maximum margin classifier using support-vector machines
- Organize the landscape of machine learning algorithms into a unified framework
- Identify the right regularizer for a given problem
- Make linear classifiers non-linear through implicit and explicit feature expansion
- Manipulate and utilize kernels to train algorithms in infinite dimensional feature spaces
Download Brochure
Fill out the form below to download program information and connect
with us.
Download a Brochure
Not ready to enroll but want to learn more? Download the course brochure to review program details.How It Works
Course Length
2 weeks
Effort
6 to 9 hours of study per week
Format
100% online, instructor-led
Course Author
Kilian Weinberger
Professor of Computer Science
Cornell Bowers Computing and Information Science
Professor of Computer Science, Cornell Computing and Information Science
Who Should Enroll
- Programmers
- Developers
- Data analysts
- Statisticians
- Data scientists
- Software engineers
Get It Done
100% Online
100% Online
Our programs are expressly designed to fit the lives of busy professionals like you.
Learn From
cornell's Top Minds
cornell's Top Minds
Courses are personally developed by faculty experts to help you gain today's most in-demand skills.
Power Your
career
career
Cornell's internationally recognized standard of excellence can set you apart.
Stack To A Certificate
Request Information Now by completing the form below.
Act today—courses are filling fast.