- CS 155: Machine Learning & Data Mining, Caltech
- CS 101: Projects in Machine Learning, Caltech, Fall 2016
- CS 159: Special Topics in Machine Learning -- Online Learning, Interactive Machine Learning, and Learning from Human Feedback, Caltech, Spring 2016
- An Introduction to Ensemble Methods: Bagging, Boosting, Random Forests and More, presented at Disney Research.
- Practical Online Retrieval Evaluation, co-taught with Filip Radlinski, presented at SIGIR 2011.
- Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI 2008.
Other Talk Materials
- Learning to Optimize for Structured Output Spaces, California Institute of Technology, January, 2017.
- The Dueling Bandits Problem, Carnegie Mellon University, October, 2016.
- Recent Applications of Latent Factor Models, Second Spectrum, September, 2015.
- Learning Spatial Models of Basketball Gameplay, KDD 2015 Workshop on Large-Scale Sports Analytics, August, 2015.
- Balancing the Explore/Exploit Tradeoff in Interactive Structured Prediction, Cornell University, December, 2014.
- Learning with Humans in the Loop, Disney Research, May, 2013.
- Optimizing Recommender Systems as a Submodular Bandit Problem, University of Toronto, November, 2012.
- An Introduction to Structural SVMs and its Application to Information Retrieval, University of California Berkeley, October, 2012.
- Practical and Reliable Retrieval Evaluation Through Online Experimentation, WSDM 2012 Workshop on Web Search Click Data, February, 2012.
- An Interactive Learning Approach to Optimizing Information Retrieval Systems, Carnegie Mellon University, September, 2010.
- New Learning Frameworks for Information Retrieval, Microsoft Research, March, 2010.
- Diversified Retrieval as Structured Prediction, SIGIR 2009 Workshop on Redundancy, Diversity and Interdependent Document Relevance, July, 2009.
- Information Retrieval as Structured Prediction, University of Massachusetts Amherst, April, 2009.