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.
Deep Learning and Neural NetworksCornell Course
Course Overview
In this course, you will investigate the fundamental components of machine learning that are used to build a neural network. You will then construct a neural network and train it on a simple data set to make predictions on new data. We then look at how a neural network can be adapted for image data by exploring convolutional networks. You will have the opportunity to explore a simple implementation of a convolutional neural network written in PyTorch, a deep learning platform. Finally, you will yet again adapt neural networks, this time for sequential data. Using a deep averaging network, you will implement a neural sequence model that analyzes product reviews to determine consumer sentiment.
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
- Learning with Kernel Machines
Key Course Takeaways
- Explore the inner workings of neural networks
- Construct and train a neural network for new prediction tasks
- Adapt neural networks to take advantage of specific properties of image data
- Adapt neural networks to take advantage of specific properties of sequence data
Download a Brochure
Not ready to enroll but want to learn more? Download the course brochure to review program details.How It Works
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Who Should Enroll
- Programmers
- Developers
- Data analysts
- Statisticians
- Data scientists
- Software engineers
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