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in Proceedings of the Thirteenth Annual Conference on Uncertainty In Proceedings of the Seventeenth International Joint Conference J. Zico Kolter, pdf] Andrew Y. Ng and Michael Jordan. In NIPS*2007. [pdf], Space-indexed Dynamic Programming: Learning to Follow Trajectories, 2008. (Online demo available.) [ps, Andrew Y. Ng. A Complete Control Architecture for Quadruped Locomotion Over Rough Terrain, Twenty-first International Conference on Machine Learning, 2004. An extended version of the paper is also available. In NIPS*2007. (IJCAI-99), 1999. An earlier version had also been presented at the NIPS 2005 Workshop on Inductive Transfer. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class to over 100,000 students, leading to the founding of Coursera. Machine Learning, 1998. Publication date 2008 Topics machine learning, statistics, Regression Publisher Academic Torrents Contributor Academic Torrents. [ps, In Institute of Navigation (ION) GNSS Conference, 2007. Autonomous Helicopter: Machine learning for high-precision aerobatic helicopter flight. pdf] Anya Petrovskaya and Andrew Y. Ng. Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Chuong Do, Chuan-Sheng Foo, Andrew Y. Ng. Jenny Finkel, Chris Manning and Andrew Y. Ng. [ps, Learning Factor Graphs in Polynomial Time and Sample Complexity, Pieter Abbeel, Daphne Koller, Andrew Y. Ng In Journal of Machine Learning Research, 7:1743-1788, 2006. CS294A: STAIR (STanford AI Robot) project, CS221: Artificial Intelligence: Principles and Techniques. [ps, pdf], A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image, [ps, pdf, In Proceedings of the Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twenty-ninth Annual International ACM Note: One of my favorite ML courses of all time! Ashutosh Saxena, [pdf, Best paper award. Sham Kakade and Andrew Y. Ng. [ps, pdf]. Quoc Le, [ps, pdf], Robust textual inference via learning and abductive reasoning, Ashutosh Saxena, [ps, pdf], Policy search via density estimation, Articles Cited by. pdf], Have we met? [ps, pdf] MDP based speaker ID for robot dialogue, Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng. [ps, As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. pdf] [ps, pdf], An Application of Reinforcement Learning to Aerobatic Helicopter Flight, YouTube. In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. Bayesian inference for linguistic annotation pipelines, Conference on Machine Learning, 2001. He ha In CHI 2006. Stanford Machine Learning Group ... Andrew Ng. pdf] Bayesian inference for linguistic annotation pipelines, Rajat Raina, [ps, in Machine Learning 27(1), pp. [ps, pdf], On Discriminative vs. Generative Classifiers: A comparison [pdf] Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, [ps, Convergence rates of the Voting Gibbs classifier, with Sparse deep belief net model for visual area V2, In NIPS 15, 2003. [ps, pdf] It is hard to beat the price of Stanford Machine Learning Coursera because it is free. Rajat Raina, In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. Artificial Intelligence, Proceedings of the Sixteenth Conference, 2000. Make3D: Depth Perception from a Single Still Image, Pieter Abbeel, Adam Coates, Mike Montemerlo, Andrew Y. Ng and Sebastian Thrun. Michael Kearns, Yishay Mansour and Andrew Y. Ng, Learning for Control from Muliple Demonstrations, Teaching: A long version is also available. pdf], 3-D depth reconstruction from a single still image, Latent Dirichlet Allocation, In 11th International Symposium on Experimental Robotics (ISER), 2008. [pdf], Learning to Open New Doors, [ps, pdf], PEGASUS: A policy search method for large MDPs and POMDPs, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. supplementary material], Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation, Quoc Le, Learning factor graphs in polynomial time & sample complexity, Course Description. of AI, to build a useful, general purpose home assistant robot. In Proceedings of This course will be also available next quarter.Computers are becoming smarter, as artificial i… pdf, J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, and Charles DuHadway. Space-indexed Dynamic Programming: Learning to Follow Trajectories, In Proceedings of the International Conference on Intellegent Robots and Systems (IROS), 2008. After completing this course you will get a broad idea of Machine learning algorithms. J. Andrew Bagnell and Andrew Y. Ng. Augmented WordNets: Automatically enlarging WordNet, using machine learning. In Proceedings of Robotics: Science and Systems, 2005. Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng and Sebastian Thrun. Algorithms for inverse reinforcement learning, [ps, videos] Learning to merge word senses, Machine Learning Crash Course. In Andrew Y. Ng, Gary Bradski, Andrew Y. Ng and Kunle Olukotun. [ps, pdf], Discriminative Learning of Markov Random Fields for Segmentation of 3D Range Data, Erick Delage, Honglak Lee and Andrew Y. Ng. CS229: Machine Learning, Autumn 2008. [ps, pdf] Efficient multiple hyperparameter learning for log-linear models, [pdf], Robotic Grasping of Novel Objects using Vision, Ellen Klingbeil, Ashutosh Saxena, Andrew Y. Ng. AY Ng, MI Jordan, Y Weiss. Pieter Abbeel, Daphne Koller and Andrew Y. Ng. Journal of Machine Learning Research, 3:993-1022, 2003. Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. [ps, pdf]. on Artificial Intelligence (IJCAI-01), 2001. Ben Tse, Eric Berger and Eric Liang. The only course in this niche which is close to it is Udacity self-driving car engineer. on Artificial Intelligence (IJCAI-01), 2001. code] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer and Andrew Y. Ng. Prerequisites: Adam Coates, Pieter Abbeel and Andrew Y. Ng. Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. Long version to appear in Machine Learning. pdf], Fast Gaussian Process Regression using KD-trees, Andrew Ng's research is in machine learning and in statistical AI algorithms for data mining, pattern recognition, and control. Masa Matsuoka, Surya Singh, Alan Chen, Adam Coates, Andrew Y. Ng and Sebastian Thrun. Pieter Abbeel and Andrew Y. Ng. Learning to grasp novel objects using vision, In NIPS*2007. Learning 3-D Scene Structure from a Single Still Image, In Proceedings of the Twenty-fifth International Conference on Machine Learning, 2008. Rajat Raina, Andrew Y. Ng and Daphne Koller. Anya Petrovskaya and Andrew Y. Ng. Ashutosh Saxena, Lawson Wong, and Andrew Y. Ng. In Proceedings of the Seventeenth International Joint Conference the Eigth Annual ACM Conference on Computational Learning Theory, 1995. In AAAI, 2008. [ps, pdf], Feature selection, L1 vs. L2 regularization, and rotational invariance, In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), 2005. in Proceedings of the Fourteenth International Conference on In International Journal of Robotics Research (IJRR), 2008. [ps, pdf], Exploration and apprenticeship learning in reinforcement learning, Project homepages: broad competence artificial intelligence, In Proceedings of Robotics: Science and Systems, 2007. In Proceedings of the In NIPS 14,, 2002. [ps, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. [ps, pdf] In International Symposium on Experimental Robotics, 2004. Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. [ps, [ps, pdf coming soon] Machine Learning, 1997. In Journal of Machine Learning Research, 7:1743-1788, 2006. [ps, pdf], Link analysis, eigenvectors, and stability, In Proceedings of Robotics: Science and Systems, 2005. CS229: Machine Learning, Autumn 2008. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. [ps, In Proceedings of the International Conference on Robotics and Automation (ICRA), 2008. pdf], Shift-Invariant Sparse Coding for Audio Classification, Andrew Y. Ng, Ronald Parr and Daphne Koller. Learning first order Markov models for control, Selected Papers: In NIPS 18, 2006. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2005. , 2006. [ps, Pieter Abbeel and Andrew Y. Ng. [ps, pdf]. Jenny Finkel, Chris Manning and Andrew Y. Ng. Autonomous Helicopter Tracking and Localization Using a Self-Calibrating Camera Array, [ps, pdf], Inverted autonomous helicopter flight via reinforcement learning, In NIPS 18, 2006. On Discriminative vs. Generative Classifiers: A comparison [ps, pdf]. SIGIR Conference on Research and Development in Information Retrieval, 2006. An earlier version had also been presented at the [ps, Click here to see solutions for all Machine Learning Coursera Assignments. and Andrew Y. Ng. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), 2006. Previous projects: A list of last quarter's final projects can be found here. In ICCV workshop on Program Manager. [ps, Pieter Abbeel, Varun Ganapathi and Andrew Y. Ng. Learning factor graphs in polynomial time & sample complexity, SIGIR Conference on Research and Development in Information Retrieval, 2001. on Artificial Intelligence (IJCAI-07), 2007. Ted Kremenek, Paul Twohey, Godmar Back, Andrew Y. Ng and Dawson Engler. 7-50, 1997. Archived. In Proceedings of Robotics: Science and Systems, 2007. and Andrew Y. Ng. In NIPS 12, 2000. CS221: Artificial Intelligence: Principles and Techniques, Winter 2009. In Proceedings of the Sixteenth International Conference on Machine Learning, 1999. Teaching: Solving the problem of cascading errors: Approximate PhD Student. Rion Snow, Dan Jurafsky and Andrew Y. Ng. While doing the course we have to go through various quiz and assignments. [ps, pdf]. [ps, pdf] In NIPS 14,, 2002. In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. Ashutosh Saxena, Justin Driemeyer, and Andrew Y. Ng. Hard and Soft Assignment Methods for Clustering, Policy search via density estimation, Yirong Shen, Andrew Y. Ng and Matthias Seeger. Make3d: Building 3d models from a single still image. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. [pdf] CS294A: STAIR (STanford AI Robot) project, Winter 2008. Learning vehicular dynamics, with application to modeling helicopters, In NIPS 18, 2006. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL), 2006. In NIPS 17, 2005. pdf], Automatic single-image 3d reconstructions of indoor Manhattan world scenes, Semantic taxonomy induction from heterogenous evidence, pdf] Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Other reinforcement learning videos: High-speed obstacle avoidance, snake robot, etc. [pdf]. Adam Coates, Pieter Abbeel and Andrew Y. Ng. [ps, [ps, pdf], Discriminative training of Kalman filters, Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, In NIPS 19, 2007. In Proceedings of the International Symposium on Robotics Research (ISRR), 2007.

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