Courses
Fall 2019: CS 760 Machine Learning
 Computational approaches to learning. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Students are strongly encouraged to have knowledge of probability, statistics, linear algebra, and calculus, and have good programming experience.
 Advanced mathematical theory and methods of machine learning. Statistical learning theory, VapnikChevronenkis Theory, model selection, highdimensional models, nonparametric methods, probabilistic analysis, optimization, learning paradigms.
 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.
 The Wisconsin AI100 Reaction Corpora (version 201609, version 201709, version 201809)
 Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of stateoftheart learning algorithms and optimization methods, and applications of machine learning. Students should have taken a course in statistics and a course in linear algebra (e.g., STAT 302 and MATH 341).
 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?
Talks
 Toward adversarial learning as control at 2nd AOR/IARPA Workshop on Adversarial Machine Learning. University of Maryland, May 2018
 Machine Teaching and its Applications. Department of Computer Science, Duke University. 2018
 Machine Teaching as a Probe for Learning Mechanism in Humans at the Tsinghua Laboratory of Brain and Intelligence Workshop on Brain and Artificial Intelligence. Beijing, China. 2017
 Debugging the Machine Learning Pipeline at the Interpretable Machine Learning Symposium, NIPS 2017
 Introduction to Machine Teaching at the Workshop on Teaching Machines, Robots, and Humans, NIPS 2017
 Dagstuhl Seminar on Machine Learning and Formal Methods. August 2017, Germany. A Challenge in Machine Teaching.
 Simons Institute Workshop on Interactive Learning 2017, Berkely, CA. Machine Teaching in Interactive Learning
 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 WisconsinMadison. 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 UniversityPurdue University Indianapolis, 2009
 HAMLET, IBM, 2009
 SemiSupervised Learning by MultiManifold Separation, IMA Minnesota, 2008
 TexttoPicture Synthesis, Carnegie Mellon University, 2008
 Online SemiSupervised Learning, 40th Symposium on the Interface: Computing Science and Statistics, 2008
 SemiSupervised Learning in Computers and Humans, University of MichiganAnn Arbor, 2007
 SemiSupervised Learning: an overview, Joint Statistical Meetings, Seattle, Washington, 2006
Tutorials
 Tutorial on Statistical Machine Learning for NLP at CCF ADL 46 in Chongqing, China, 2013.
 Tutorial on Understanding Social Media with Machine Learning at CCF ADL 39 in Beijing, China, 2013.
 Tutorial on Graphical Models at KDD 2012.
 Tutorial on All of Graphical Models at The tenth International Conference on Machine Learning and Applications (ICMLA11), 2011.
 Tutorial on SemiSupervised Learning at University of Chicago Machine Learning Summer School 2009
 Tutorial on SemiSupervised Learning for Natural Language Processing at ACL 2008 with John Blitzer
 Tutorial on SemiSupervised Learning at ICML 2007
 Tutorial and practice session on SemiSupervised Classification: learning from labeled and unlabeled data at AERFAI Summer School. Granada, 2006
Local interest
 Machine Learning @ University of WisconsinMadison
 How University of WisconsinMadison Computer Science Graduate Program Works, Approximately
 Future CS timetables.
 HAMLET (Human, Animal, and Machine Learning: Experiment and Theory). Old archive
 Fall 2011 Meet the AI Group (organized by Bryan Gibson).
 Spring 2011 Structured Sparsity Reading Group (organized by Junming Xu).
 Fall 2010 Noni.i.d. Learning Reading Group (organized by KwangSung Jun).
 Spring 2010 Learning Math for Machine Learning (LMML) Reading Group (organized by David Andrzejewski)
 Fall 2009 Bayesian Nonparametrics Reading Group (organized by Andrew Goldberg)
Random stuff

Excellent, free museums within walking distance on campus:
 Geology Museum: fossils & minerals
 Chazen Museum of Art: paintings & sculpture
 L.R. Ingersoll Physics Museum: handson physics
 Not sure how to pronounce Chinese names like Zhu, Cai, Qin, Xu? Learn it in five minutes
 Build a spectroscope from a CD and a cereal box
 Aurora Borealis! How to See the Northern Lights from Wisconsin and Elsewhere
 How to observe the flash spectrum during total solar eclipse