Kilian Weinberger is an Associate 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 Computer Science from the University of Oxford. During his career he has won several best paper awards at ICML (2004), CVPR (2004, 2017), AISTATS (2005) and KDD (2014, runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected co-Program Chair for ICML 2016 and for AAAI 2018. In 2016 he was the recipient of the Daniel M Lazar ’29 Excellence in Teaching Award. Kilian Weinberger’s research focuses on Machine Learning and its applications. In particular, he focuses on learning under resource constraints, metric learning, machine learned web-search ranking, computer vision 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.
Overview
How It Works
Format
All Online
Time Commitment
3.5 months with 6-9 hours of study per week
Cost
$0
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
Format
All Online
Time Commitment
3.5 months with 6-9 hours of study per week
Cost
$0
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
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Faculty Author
Kilian Weinberger
Associate Professor
Cornell Bowers Computing and Information Science
Associate Professor, Cornell Computing and Information Science
Key Course Takeaways
- Redefine problems using machine learning concepts and terminology
- Create a face recognition system using a simple algorithm
- Estimate probabilities distributions from data and implement the naive Bayes algorithm to create a name classifier
- Apply convex optimization and implement a linear classifier to create an email spam filter
- Use effective hyperparameter search to select a well-suited machine learning model and implement a machine learning setup from start to finish
- Improve the prediction accuracy of an algorithm using bias-variance trade-off
- Extend the applicability of linear classifiers to learn non-linear decision boundaries from more complex datasets
- Train a neural network that achieves cutting-edge accuracy by incorporating appropriate assumptions about your data
- Build generative models to create text and image outputs

“
Completing a program from eCornell really has allowed me to think outside the box at work. It gave me the confidence I needed to take a seat at that table and say I am ready.
‐ Kasey M.

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