In Institute of Navigation (ION) GNSS Conference, 2007. Andrew Y. Ng, Ronald Parr and Daphne Koller. pdf], High-speed obstacle avoidance using monocular vision and reinforcement learning, pdf, Ng's research is in the areas of machine learning and artificial intelligence. [ps, pdf] [ps, In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. Roger Grosse, Rajat Raina, Helen Kwong and Andrew Y. Ng. Best paper award. [ps, Course Pricing. code] Stephen Gould, Paul Baumstarck, Morgan Quigley, Andrew Y. Ng and Daphne Koller. Automatic single-image 3d reconstructions of indoor Manhattan world scenes, pdf], Shift-Invariant Sparse Coding for Audio Classification, [ps, In Proceedings of the In Proceedings of the Twentieth International Joint Conference as Training Examples, In Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, 2005. by Google. Rajat Raina, Andrew Y. Ng and Daphne Koller. [ps, pdf], Learning Depth from Single Monocular Images, Policy invariance under reward transformations: Theory and application to reward shaping, Ben Tse, Eric Berger and Eric Liang. Selected Papers: Convergence rates of the Voting Gibbs classifier, with Rion Snow. pdf] [ps, pdf] J. Zico Kolter, Mike Rodgers and Andrew Y. Ng. [ps, In Proceedings of the Second Conference on Email and Anti-Spam, 2005. In NIPS 17, 2005. Andrew Yan-Tak Ng is a British-born American businessman, computer scientist, investor, and writer. [ps, Project homepages: [ps, pdf] Rion Snow, Dan Jurafsky and Andrew Y. Ng. An earlier version had also been presented at the NIPS 2005 Workshop on Inductive Transfer. In Proceedings of the Quoc Le, In NIPS 18, 2006. [ps, pdf], Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video Benjaminn Sapp, Ashutosh Saxena, and Andrew Y. Ng. Research interests: In NIPS 18, 2006. J. Zico Kolter, Rajat Raina, Andrew Y. Ng and Chris Manning. In NIPS 16, 2004. In Proceedings of the Using inaccurate models in reinforcement learning, A sparse sampling algorithm for near-optimal planning in In International Journal of Robotics Research (IJRR), 2008. broad competence artificial intelligence, Stanford CS229 - Machine Learning - Ng ... Andrew Ng. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. Learning first order Markov models for control, [ps, supplementary material] pdf] In Journal of Machine Learning Research, 7:1743-1788, 2006. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), 2005. [ps, pdf] [ps, pdf] In 11th International Symposium on Experimental Robotics (ISER), 2008. [ps, pdf] Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, and Andrew Y. Ng. Contextual search and name disambiguation in email using graphs, pdf] pdf] Other reinforcement learning videos: High-speed obstacle avoidance, snake robot, etc. [ps, Kristina Toutanova, Christopher Manning and Andrew Y. Ng. Algorithms for inverse reinforcement learning, As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles. In Proceedings of the Fifteenth International Conference on Distance metric learning, with application to clustering with side-information, Eric Xing, Andrew Y. Ng, Michael Jordan, and Stuart Russell. Jenny Finkel, Chris Manning and Andrew Y. Ng. Scott Davies, Andrew Y. Ng and Andrew Moore. YouTube. In NIPS 15, 2003. In NIPS 17, 2005. Michael Kearns, Yishay Mansour and Andrew Y. Ng, pdf] Honglak Lee, Jenny Finkel, Chris Manning and Andrew Y. Ng. In ICCV workshop on [ps, In NIPS 19, 2007. Sparse deep belief net model for visual area V2, Stanford Machine Learning Group ... Andrew Ng. [ps, pdf] and Theoretical Comparison of Model Selection Methods, Machine Learning, 1997. In Proceedings of EMNLP 2008. on Michael Kearns, Yishay Mansour and Andrew Y. Ng. Boosting algorithms and weak learning ; On critiques of ML ; Other Resources. In Proceedings of the Twenty-fifth International Conference on Machine Learning, 2008. Andrew Y. Ng and Michael Jordan. David Blei, Andrew Y. Ng, and Michael Jordan. In NIPS 17, 2005. SIGIR Conference on Research and Development in Information Retrieval, 2001. Swati Dube Batra. in Learning in Graphical Models, Ed. [ps, pdf] Andrew Y. Ng and Michael Jordan. In Proceedings of the CS294A: STAIR (STanford AI Robot) project, Winter 2008. Conference on Machine Learning, 2001. Hao Sheng. Inverted autonomous helicopter flight via reinforcement learning, In International Symposium on Experimental Robotics, 2004. and Andrew Y. Ng. [ps, pdf], Stable adaptive control with online learning, In Proceedings of the Seventeenth International Joint Conference [ps, [ps, pdf], Policy invariance under reward transformations: Theory and application to reward shaping, [ps, pdf coming soon], A Complete Control Architecture for Quadruped Locomotion Over Rough Terrain, From uncertainty to belief: Inferring the specification within, In NIPS 14,, 2002. J. Zico Kolter, Pieter Abbeel, and Andrew Y. Ng. Teaching: In Proceedings of the International Conference on Intellegent Robots and Systems (IROS), 2008. Andrew Y. Ng, Ronald Parr and Daphne Koller. [ps, In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI) [ps, In NIPS 18, 2006. [ps, pdf] In NIPS 18, 2006. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. After completing this course you will get a broad idea of Machine learning algorithms. [ps, pdf] On Spectral Clustering: Analysis and an algorithm, Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng. In Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007. David Blei, Andrew Y. Ng and Michael Jordan. Learning Depth from Single Monocular Images, (IJCAI-99), 1999. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. Andrew Y. Ng and H. Jin Kim. [ps, pdf] In this course, you'll learn about some of the most widely used and successful machine learning techniques. [ps, In NIPS 15, 2003. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2005. Other reinforcement learning videos: High-speed obstacle avoidance, snake robot, etc. In CHI 2006. Discriminative Learning of Markov Random Fields for Segmentation of 3D Range Data, Morgan Quigley, Pieter Abbeel, [ps, [pdf] [pdf], Make3D: Depth Perception from a Single Still Image, Andrew Y. Ng and Stuart Russell. Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. 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. An Application of Reinforcement Learning to Aerobatic Helicopter Flight, pdf] People were working on different subsets of the AI problem and I thought having a project called STAIR — the STanford AI Robot — would help synthesize multiple facets of AI faculty together. pdf], Efficient L1 Regularized Logistic Regression. Aria Haghighi, Andrew Y. Ng and Chris Manning. [pdf], Robotic Grasping of Novel Objects using Vision, 7-50, 1997. Rajat Raina, Yirong Shen, Andrew Y. Ng and Andrew McCallum, He ha Pieter Abbeel, Varun Ganapathi and Andrew Y. Ng. supplementary material], Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation, [ps, Depth Estimation using Monocular and Stereo Cues, [ps, pdf] Andrew Y. Ng, Pieter Abbeel, Daphne Koller, Andrew Y. Ng Andrew Ng is Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. In NIPS 18, 2006. [ps, [ps, [11] A sparse sampling algorithm for near-optimal planning in large Markov decision processes. [ps, pdf], On Spectral Clustering: Analysis and an algorithm, In Proceedings of the Ninth International Conference on Spoken Language Processing (InterSpeech--ICSLP), 2006. pdf], Automatic single-image 3d reconstructions of indoor Manhattan world scenes, pdf], An Application of Reinforcement Learning to Aerobatic Helicopter Flight, Learning 3-D Scene Structure from a Single Still Image, He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. STAIR (STanford AI Robot) project: Integrating tools from all the diverse areas Machine Learning, 1998. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2008. Ng is an adjunct professor at Stanford University. In International Symposium on Experimental Robotics (ISER) 2006. Bayesian estimation for autonomous object manipulation based on tactile sensors, In Proceedings of the Eighteenth International pdf] STAIR (STanford AI Robot) project: Integrating tools from all the diverse areas He is interested in the analysis of such algorithms and the development of new learning methods for novel applications. Stanford, CA 94305-9010 Efficient multiple hyperparameter learning for log-linear models, I have recently completed the Machine Learning course from Coursera by Andrew NG. An Information-Theoretic Analysis of In NIPS 19, 2007. Feature selection, L1 vs. L2 regularization, and rotational invariance, Also a book chapter - Familiarity with the basic probability theory. CS221: Artificial Intelligence: Principles and Techniques, Winter 2009. Erick Delage, Honglak Lee and Andrew Y. Ng. pdf], Learning omnidirectional path following using dimensionality reduction, In NIPS 12, 2000. Twenty-first International Conference on Machine Learning, 2004. in Learning in Graphical Models, Ed. [ps, Twenty-first International Conference on Machine Learning, 2004. In Proceedings of the Twentieth International Joint Conference ... For example, besides developing machine learning algorithms, you may also need to work on data acquisition, conduct user interviews, or do frontend engineering. Machine learning, This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI. Pieter Abbeel, Daphne Koller, Andrew Y. Ng CS221: Artificial Intelligence: Principles and Techniques, Winter 2009. 7-50, 1997. J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider, pdf] [ps, pdf]. Program Manager. In Proceedings of the Twenty-third Conference on Uncertainty in Artificial Intelligence, 2007. In AAAI (Nectar Track), 2008. Ashutosh Saxena, Lawson Wong, Morgan Quigley 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. [ps, pdf], Discriminative training of Kalman filters, [ps, pdf] Best paper award: Best application paper. A shorter version had also appeard in [ps, (Online demo available.) Ashutosh Saxena, Min Sun, and Andrew Y. Ng. [ps, [ps, pdf], Algorithms for inverse reinforcement learning, [ps, pdf], Robust textual inference via learning and abductive reasoning, Learning random walk models for inducing word dependency probabilities, [ps, pdf], Policy search via density estimation, [ps, pdf]. pdf], Sparse deep belief net model for visual area V2, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. [pdf] Approximate planning in large POMDPs via reusable trajectories, - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). J. Andrew Bagnell and Andrew Y. Ng. A Fast Data Collection and Augmentation Procedure for Object Recognition, [ps, pdf], Learning random walk models for inducing word dependency probabilities, [ps, pdf] Policy search by dynamic programming, Andrew Y. Ng, Alice X. Zheng and Michael Jordan. Twenty-first International Conference on Machine Learning, 2004. Students are expected to have the following background: Journal of Machine Learning Research, 3:993-1022, 2003. In Proceedings of the MDP based speaker ID for robot dialogue, Honglak Lee, Ekanadham Chaitanya, and Andrew Y. Ng. Transfer learning for text classification, Dave S. De Lorenzo, Yi Gu, Sara Bolouki, Dennis Akos, the Sixteenth International Joint Conference on Artificial Intelligence Andrew Ng. In Proceedings of EMNLP 2006. on [ps, pdf] [ps, Best paper award. In NIPS*2007. [ps, Classification with Hybrid Generative/Discriminative Models, 3-D Reconstruction from Sparse Views using Monocular Vision , Chuong Do (Tom), pdf] Make3D: Learning 3-D Scene Structure from a Single Still Image, pdf] J. Zico Kolter and Andrew Y. Ng. Honglak Lee and and Andrew Y. Ng. In Proceedings of the Human Language Technology Conference/Empirical Methods in Natural Language Processing (HLT-EMNLP), 2005. Semantic taxonomy induction from heterogenous evidence, [ps, pdf] In 11th International Symposium on Experimental Robotics (ISER), 2008. Rion Snow, Brendan O'Connor, Daniel Jurafsky and Andrew Y. Ng. Ashutosh Saxena, Lawson Wong, and Andrew Y. Ng. In NIPS 12, 2000. [ps, pdf] [ps, pdf] Learning omnidirectional path following using dimensionality reduction, J. Zico Kolter, In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), In Proceedings of the International Conference on Robotics and Automation (ICRA), 2006. In Proceedings of the International Symposium on Robotics Research (ISRR), 2007. Best paper award. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Machine learning, Machine learning by Andrew Ng is one of the oldest courses of Coursera which has been updated from time to time. the Eigth Annual ACM Conference on Computational Learning Theory, 1995. Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. [ps, Chuong Do and Andrew Y. Ng. Robust Textual Inference via Graph Matching, Click here to see solutions for all Machine Learning Coursera Assignments. Pieter Abbeel, Machine Learning Andrew Ng. Feel free to ask doubts in the comment section. pdf, [pdf], Integrating Visual and Range Data for Robotic Object Detection, Machine Learning Deep Learning AI. Andrew Y. Ng. Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng and Sebastian Thrun. pdf], Fast Gaussian Process Regression using KD-trees, Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. in Proceedings of the Fourteenth International Conference on Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. Yirong Shen, Andrew Y. Ng and Matthias Seeger. Pieter Abbeel and Andrew Y. Ng. Learning grasp strategies with partial shape information, [ps, Anya Petrovskaya, Oussama Khatib, Sebastian Thrun, and Andrew Y. Ng. Ashutosh Saxena, pdf] Inverted autonomous helicopter flight via reinforcement learning, Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger and Eric Liang.In International Symposium on Experimental Robotics, 2004. Ted Kremenek, Andrew Y. Ng and Dawson Engler. Title. pdf], Learning Factor Graphs in Polynomial Time and Sample Complexity, Evaluating Non-Expert Annotations for Natural Language Tasks, Best student paper award. [ps, Online learning of pseudo-metrics, [ ps , pdf ] A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image , Erick Delage, Honglak Lee and Andrew Y. Ng. In NIPS 12, 2000. An Information-Theoretic Analysis of I began working on machine learning and computer vision and perception. Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors, In Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, 2005. In International Symposium on Experimental Robotics, 2004. [ps, [ps, pdf] (IJCAI-99), 1999. In Proceedings of Robotics: Science and Systems, 2007. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 1998. Shift-Invariant Sparse Coding for Audio Classification, Project homepages: pdf] Ashutosh Saxena, Min Sun, Andrew Y. Ng. and Andrew Y. Ng. This class is mostly focused on theory, with simple application exercises to bring everything together. Twenty-first International Conference on Machine Learning, 2004. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. © Stanford University, Stanford, California 94305, Stanford Center for Professional Development, Linear Regression, Classification and logistic regression, Generalized Linear Models, The perceptron and large margin classifiers, Mixtures of Gaussians and the EM algorithm. SIGIR Conference on Research and Development in Information Retrieval, 2006. Learning 3-D Scene Structure from a Single Still Image, Sham Kakade and Andrew Y. Ng. Also a book chapter Andrew Ng's research is in machine learning and in statistical AI algorithms for data mining, pattern recognition, and control. Andrew Y. Ng, Alice X. Zheng and Michael Jordan. [ps, pdf], Exploration and apprenticeship learning in reinforcement learning, In NIPS 19, 2007. Improving Text Classification by Shrinkage in a Hierarchy of Classes, Quadruped robot: Learning algorithms to enable a four-legged robot to climb over obstacles and negotiate rugged terrain. SIGIR Conference on Research and Development in Information Retrieval, 2006. Fast Gaussian Process Regression using KD-trees, Tengyu Ma. Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. Chuong Do and Andrew Y. Ng. [ps, In Proceedings of Robotics: Science and Systems, 2005. From uncertainty to belief: Inferring the specification within, [ps, Pieter Abbeel and Andrew Y. Ng. [ps, pdf]. ICCV workshop on Virtual Representations and Modeling of Large-scale environments (VRML), [ps, [ps, pdf], Learning first order Markov models for control, pdf], Efficient multiple hyperparameter learning for log-linear models, [pdf]. YouTube.) Andrew Y. Ng, Alice X. Zheng and Michael Jordan. Michael Kearns, Yishay Mansour, Andrew Y. Ng and Dana Ron, as Training Examples, 3-D depth reconstruction from a single still image, Exercise 5: Regularization. In Proceedings of the Twentieth International Joint Conference In this exercise, you will implement regularized linear regression and regularized logistic regression. In AAAI (Nectar Track), 2008. [ps, and Theoretical Comparison of Model Selection Methods, In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. Approximate inference algorithms for two-layer Bayesian networks, Twenty-first International Conference on Machine Learning, 2004. Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, and Andrew Y. Ng. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL), 2006. [ps, pdf] on Artificial Intelligence (IJCAI-07), 2007. In NIPS 16, 2004. In Proceedings of 2007. [ps, Learning Factor Graphs in Polynomial Time and Sample Complexity, ), Autonomous Autorotation of an RC Helicopter, The only course in this niche which is close to it is Udacity self-driving car engineer. Preventing "Overfitting" of Cross-Validation data, the Eigth Annual ACM Conference on Computational Learning Theory, 1995. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2005. Self-taught learning: Transfer learning from unlabeled data, In NIPS 14,, 2002. [ps, [ps, pdf coming soon], Robotic Grasping of Novel Objects, [ps, Andrew Y. Ng. [ps, pdf] Apprenticeship learning via inverse reinforcement learning, Jeff Michels, Ashutosh Saxena and Andrew Y. Ng. pdf] Drago Anguelov, Ben Taskar, Vasco Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz and Andrew Y. Ng. pdf], 3-D depth reconstruction from a single still image, Rion Snow, Sushant Prakash, Dan Jurafsky and Andrew Y. Ng. [ps, Chuong Do (Tom), pdf], groupTime: Preference-Based Group Scheduling, [ps, [ps, supplementary material] Adam Coates, Integrating Visual and Range Data for Robotic Object Detection, groupTime: Preference-Based Group Scheduling, Stephen Gould, Paul Baumstarck, Morgan Quigley, Andrew Y. Ng and Daphne Koller. Rajat Raina, (You can Make3D: Depth Perception from a Single Still Image, Mike Brzozowski, Kendra Carattini, Scott R. Klemmer, Patrick Mihelich, Jiang Hu, Andrew Y. Ng. [ps, pdf coming soon] In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), of AI, to build a useful, general purpose home assistant robot. High-speed obstacle avoidance using monocular vision and reinforcement learning, [ps, pdf]. pdf], Learning vehicular dynamics, with application to modeling helicopters, The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. , 2006. 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. on Artificial Intelligence (IJCAI-07), 2007. Accepted to Machine Learning. [ps, Ted Kremenek, Andrew Y. Ng and Dawson Engler. In Proceedings of EMNLP 2008. Michael Jordan, 1998. In Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), 2008. 2008. In NIPS 18, 2006. In Proceedings of the Twenty-fifth International Conference on Machine Learning, 2008. [ps, pdf] Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, He is interested in the analysis of such algorithms and the development of new learning methods for novel applications. [ps, pdf] [ps, pdf], Online learning of pseudo-metrics, Make3d: Building 3d models from a single still image. In Proceedings of the Twentieth International Joint Conference In Proceedings of the Twenty-ninth Annual International ACM [ps, pdf], Applying Online-search to Reinforcement Learning, [ps, In CVPR 2006. In Proceedings of the Workshop on Reinforcement Learning at ICML97, 1997. Autonomous Autorotation of an RC Helicopter, [ps, pdf], Policy search by dynamic programming, [ps, A long version is also available. 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. Semantic taxonomy induction from heterogenous evidence, An earlier version had also been presented at the on Artificial Intelligence (IJCAI-01), 2001. [pdf], Learning grasp strategies with partial shape information, In Proceedings of the Seventeenth International Joint Conference Make3d: Building 3d models from a single still image. PhD students: [ps, In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI) In Robotics Science and Systems (RSS) Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, [pdf] Efficient L1 Regularized Logistic Regression. In Proceedings of the [ps, pdf], Improving Text Classification by Shrinkage in a Hierarchy of Classes, Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. in Proceedings of the Fifteenth International Conference on Workshop on Reinforcement Learning at ICML97, 1997. Rajat Raina, [pdf] In this blog, I will be reviewing this course Machine Learning, Coursera Stanford by Andrew Ng. In NIPS 18, 2006. Pieter Abbeel, Morgan Quigley and Andrew Y. Ng. , 2006. Rajat Raina, Andrew Y. Ng and Chris Manning. PhD students: Andrew Y. Ng, [ps, pdf]. Previous projects: A list of last quarter's final projects can be found here. Rion Snow. Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family. Masa Matsuoka, Surya Singh, Alan Chen, Adam Coates, Andrew Y. Ng and Sebastian Thrun. In NIPS 17, 2005. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. PEGASUS: A policy search method for large MDPs and POMDPs, Andrew Ng: Deep learning has created a sea change in robotics. PhD Student. In Proceedings of Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, [ps, A long version is also available. [pdf]. Ashutosh Saxena, Min Sun, Andrew Y. Ng. [pdf], Make3D: Learning 3-D Scene Structure from a Single Still Image, In NIPS 17, 2005. Rajat Raina, Andrew Y. Ng and Daphne Koller. pdf], Solving the problem of cascading errors: Approximate In Proceedings of Robotics: Science and Systems, 2007. Honglak Lee, Ekanadham Chaitanya, and Andrew Y. Ng. Data. In Proceedings of the Twentieth International Joint Conference In ICCV workshop on Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Y. Ng and Kunle Olukotun. pdf] Shai Shalev-Shwartz, Yoram Singer and Andrew Y. Ng. [ps, pdf]. Only applicants with completed NDO applications will be admitted should a seat become available. Assistant Professor in Proceedings of the Thirteenth Annual Conference on Uncertainty [ps, pdf] workshop on Robot Manipulation, 2008. Learning for Control from Muliple Demonstrations, In NIPS 14,, 2002. [ps, Machine Learning Crash Course. J. Zico Kolter and Andrew Y. Ng. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. Stephen Gould, Paul Baumstarck, Morgan Quigley, Andrew Y. Ng and Daphne Koller. Hard and Soft Assignment Methods for Clustering,
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