Fall 2016: CS540 Introduction to Artificial Intelligence
Explore the fascinating world of artificial intelligence! This is the undergraduate AI course. Topics include search, logic, probabilistic inference, decision trees, neural networks, support vector machines, and game theory.
- Students interested in doing research in machine learning may also consider
CS/ECE/ME 532 Theory and Applications of Pattern Recognition,
CS 760 Machine Learning,
Stat 771 Computational Statistics.
- The Wisconsin AI100 Reaction Corpus 1.0
Spring 2017: CS761 Advanced Machine Learning
CS769 Advanced Natural Language Processing
Advanced computational approaches to learning. Quantification of learnability and rate of learning, probabilistic and other formalisms of learning, statistical and computational analysis of learning models, state-of-the-art learning algorithms.
This graduate course covers statistical methods for processing natural text.
Some questions discussed in class:
How does Google work?
How many bits are there in each English word?
Can your computer learn to laugh at a joke?
Did Shakespeare write this book?
Where did "The vodka is good, but the meat is rotten" come from?
- ICML 2016 Workshop on Reliable Machine Learning in the Wild, New York. Machine Teaching and Security
- The Center for Information and Systems Engineering, Boston University. 2016.
- Department of Statistics, University of Indiana. 2016.
- Department of Computer Science and Engineering, University of Washington. Machine Teaching. 2016.
- College of Computer Science, Northeastern University. 2016.
- NIPS 2015 Workshop on Machine Learning from and for Adaptive User Technologies, Montreal, Canada. Machine Teaching for Personalized Education, Security, Interactive Machine Learning
ICML 2015 Workshop on Machine Learning for Education, Lille, France. Machine Teaching. 2015.
Systems Information Learning Optimization (SILO), University of Wisconsin-Madison. An Approach to Bridge Topology and Machine Learning, 2014.
Signatures workshop. Topological Kernels, 2014.
Simons Institute Workshop on Spectral Algorithms: From Theory to Practice. Some Applications in Human Behavior Modeling, 2014
Keynote at ICML 2014 Workshop on Learning, Security and Privacy, Beijing China. Optimal Training Set Attacks on Machine Learning. 2014.
Institute of Computer Science and Technology, Peking University. Corpus Attacks on Natural Language Processing and Machine Learning. 2014
Department of Computer Science, Tsinghua University. Maximally Influencing Learning by Machine Teaching. 2014
Department of Computer and Information Science, The University of Oregon. Machine Teaching: Frenemy of Machine Learning. 2014.
School of Electrical Engineering and Computer Science Colloquium, Oregon State University. Machine Teaching: Frenemy of Machine Learning. 2014.
Statistics colloquium, Department of Statistics, The University of Chicago.
Machine Teaching: Frenemy of Machine Learning. 2014.
- Distinguished Speaker Series, Department of Computer Science, University of Virginia. How to Make Machines Learn: Passive, Active, and Teaching. 2014.
- Duke University Machine Learning Seminar Series. Machine Teaching: Frenemy of Machine Learning. 2014.
Some Mathematical Models to Turn Social Media into Knowledge, Keynote at The Second Conference on Natural Language Processing & Chinese Computing, Chongqing, China 2013
How can a Machine Learn: Passive, Active, and Teaching, Chongqing University, China 2013
Three Assertions about Interactive Machine Learning, Microsoft Research Faculty Summit, 2013
Beyond Label Propagation, ICML 2013 Classic Paper Prize talk, 2013
Capacity, Learning, Teaching, NSF Workshop on Integrating Approaches to Computational Cognition, 2013
Can Machine Learning Rationalize Simple Human Teaching Behaviors?, UCSD Optimal Teaching Workshop, 2012
- Machine Learning Theory by the People, for the People, of the People, Machine Learning and Applications Seminar, Purdue, 2011
- Adding Domain Knowledge to Latent Topic Models, University of Massachusetts Amherst, 2011
- Is Machine Learning the Wrong Name?, Boston University, 2010
- Computers Discover Wishes and Creativity in Text, Indiana University-Purdue University Indianapolis, 2009
- HAMLET, IBM, 2009
- Semi-Supervised Learning by Multi-Manifold Separation, IMA Minnesota, 2008
- Text-to-Picture Synthesis, Carnegie Mellon University, 2008
- Online Semi-Supervised Learning, 40th Symposium on the Interface: Computing Science and Statistics, 2008
- Semi-Supervised Learning in Computers and Humans, University of Michigan--Ann Arbor, 2007
- Semi-Supervised Learning: an overview, Joint Statistical Meetings, Seattle, Washington, 2006