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.
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
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How It Works
Who Should Enroll
- Data analysts
- Data scientists
- Software engineers