CMU CS Machine Learning Group
The Machine Learning Group is part of the Center for Automated Learning and Discovery (CALD), an interdisciplinary center that pursues research on learning, data analysis and discovery.
``Ever since computers were invented, it has been natural to
wonder whether they might be made to learn. If we could
understand how to program them to learn the impact would be dramatic. The
practice of computer programming would be revolutionized as many
tedious hand-coding tasks would be replaced by automatic learning
methods. And a successful understanding of how to make computers
learn would most likely yield a better understanding of human
learning abilities and disabilities as well.''
(from Tom Mitchell's new book ``Machine Learning'')
| Avrim Blum | Interested in machine learning theory, algorithm design and analysis.
See his papers. |
| Jaime Carbonell | Interested in integrated intelligent systems
(seamless integration of machine learning, planning, problem-solving,
execution monitoring and communication), and in multi-lingual natural
language processing. |
| Scott Fahlman | Interested in artificial neural networks, software development
environments (Gwydion/Dylan), and high-performance processing of biomedical
images. |
| Tom Mitchell | Interested in machine learning, with applications to robotics, information retrieval and database mining. |
| John Lafferty | Interested in speech and natural language processing, statistical learning algorithms, probability and information theory. |
| Andrew W. Moore | Interested in machine learning applications to robots, factories
and other complex control systems. |
| Reid Simmons | Interested in mobile robot planning and task-level control, probabilistic planning and reasoning intelligent agents. |
| Katia Sycara | Interested in distributed coordination of intelligent software agents, case-based reasoning and learning, and constraint-directed reasoning. |
| Sebastian Thrun | Interested in machine learning applications to robotics,
learning architectures, reinforcement learning,
and the integration of symbolic and neural computation.
See his papers. |
| Dave Touretzky | Interested in representation and processing of information in the brain. |
| Raul Valdes-Perez | Scientific discovery and computers, e.g., interactive
programs to carry out scientific reasoning at its highest levels.
Practical deployment of tools as scientist's assistants.
Implications for future organization of science. |
| Manuela Veloso | Interested in planning and learning, machine
learning applied to signal understanding, experience-based autonomous
agents with high-level and low-level task reasoning, collaborative and
adversarial planning and learning in dynamic domains. |
| Alex Waibel | Interested in speech, language, speech translation,
multimodal interfaces, neural nets and machine learning. |
| Michael Witbrock |
| Michael Cox | Interested in case-based learning, derivational analogy, multistrategy learning, rationale capture and replay in planning, memory (especially forgetting), introspection. |
| Jeff Schneider |
| Deb Baker | Douglas Beeferman |
| Jim Blythe |
Justin Boyan |
| Aaron Courvile | Scott Davies |
| Nathaniel Daw | Frank Dellaert |
| Kan Deng |
Rujith DeSilva |
| Robert Driskill |
Eugene Fink |
| Dayne Freitag |
Mark Fuhs |
| Matthew Glickman | Rich Goodwin |
| Geoffrey Gordon | Karen Haigh |
| Angela Kennedy | Sven Koenig |
| Ken Lang | Shyjan Mahamud |
| Daniel Nikovski | Scott Reilly |
| Henry Rowley |
Sean Slattery |
| Yury Smirnov | Peter Stone |
| Joseph O'Sullivan |
Eric "Astro" Teller |
| Belinda Thom |
James Thomas |
| William Uther |
Santosh Vempala |
| Thorsten Joachims | Stefan Waldherr |
| Other visitors: please send me mail |
| Learning from Text and the Web | See Text Learning Lab Homepage |
| Learning noisy linear separators | Contact Avrim Blum |
| Algorithms for focusing on relevant features | Contact Avrim Blum |
| CRAWL | A computational theory of rodent navigation, including neural
representations of spatial information and the learning of new environments
by exploration. Contact Dave Touretzky or Mark Fuhs |
| XAVIER and AMELIA | Autonomous mobile robots that learn |
| The Skinnerbots project | A theory of operant conditioning with application to trainable robots. Contact Dave Touretzky, Aaron Courvile, or Nathaniel Daw |
| WebWatcher | A tour guide for the World Wide Web |
| PRODIGY | A high-speed planning and learning architecture |
| Detection of faces and other objects in images, using neural network-based techniques | See the on-line demo |
| Various projects related to speech, language, speech translation, and multimodal interfaces | |
| AUTON |
|
| Planning and Learning by Analogical/Case-Based Reasoning |
|
| Planning and Search Algorithms |
|
| Minds for Robots: Agents that Plan, Execute, and Learn |
|
| Collaborative and Adversarial Planning and Learning:
RoboSoccer and
Bolo |
|
| Machine Learning for Signal Understanding |
|
| Rationale Capture and Reuse in Mixed-Initiative Planning |
maintained by thrun@cs.cmu.edu