dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Rajat Raina, Pieter Abbeel, Daphne Koller, Andrew Y. Ng Honglak Lee and and Andrew Y. Ng. Andrew Ng's research is in machine learning and in statistical AI algorithms for data mining, pattern recognition, and control. In this blog, I will be reviewing this course Machine Learning, Coursera Stanford by Andrew Ng. [ps, pdf] Course Description You will learn to implement and apply machine learning algorithms.This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. Andrew Y. Ng, Alice X. Zheng and Michael Jordan. In CVPR 2006. [ps, pdf]. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. Dave S. De Lorenzo, Yi Gu, Sara Bolouki, Dennis Akos, The only course in this niche which is close to it is Udacity self-driving car engineer. [ps, pdf] [ps, pdf], Classification with Hybrid Generative/Discriminative Models, [ps, pdf], Cheap and Fast - But is it Good? Stanford University In Proceedings of the International Symposium on Robotics Research (ISRR), 2005. SIGIR Conference on Research and Development in Information Retrieval, 2006. Andrew Y. Ng and Michael Jordan. In Proceedings of the AY Ng, MI Jordan, Y Weiss. CS229: Machine Learning, Autumn 2008. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. Also a book chapter 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. Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, and Andrew Y. Ng. [ps, in Proceedings of the Fourteenth International Conference on [ps, Click here to see more codes for NodeMCU ESP8266 and similar Family. In 11th International Symposium on Experimental Robotics (ISER), 2008. [ps, pdf] This course will be also available next quarter.Computers are becoming smarter, as artificial i… Andrew Ng He was also Chief Scientist at Baidu Inc., and Founder & Lead for the Google Brain Project. Masa Matsuoka, Surya Singh, Alan Chen, Adam Coates, Andrew Y. Ng and Sebastian Thrun. Improving Text Classification by Shrinkage in a Hierarchy of Classes, Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng. Pieter Abbeel and Andrew Y. Ng. In Proceedings of the Twentieth International Joint Conference In NIPS*2007. In Proceedings of the Twenty-ninth Annual International ACM 3-D Reconstruction from Sparse Views using Monocular Vision , [pdf], Learning grasp strategies with partial shape information, Machine Learning, 1998. Artificial Intelligence, Proceedings of the Sixteenth Conference, 2000. In Proceedings of the International Symposium on Robotics Research (ISRR), 2007. pdf] [ps, pdf], Inverted autonomous helicopter flight via reinforcement learning, Andrew Y. Ng, An earlier version had also been presented at the Machine learning, Teaching: [pdf], Robotic Grasping of Novel Objects using Vision, Andrew Y. Ng, Alice X. Zheng and Michael Jordan. In International Journal of Robotics Research (IJRR), 2008. [pdf]. Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer and Andrew Y. Ng. Link analysis, eigenvectors, and stability, In AAAI (Nectar Track), 2008. [ps, pdf] Approximate inference algorithms for two-layer Bayesian networks, Cheap and Fast - But is it Good? [ps, pdf], Learning random walk models for inducing word dependency probabilities, [ps, pdf], Online bounds for Bayesian algorithms, Course Description. In Proceedings of the Eighteenth International Students are expected to have the following background: Accepted to Machine Learning. Note: One of my favorite ML courses of all time! [ps, pdf] pdf], Robust Textual Inference via Graph Matching, Learn Machine Learning from Stanford University. [pdf] on Artificial Intelligence (IJCAI-07), 2007. [ps, Discriminative training of Kalman filters, Other reinforcement learning videos: High-speed obstacle avoidance, snake robot, etc. In AAAI, 2008. [ps, pdf], Preventing "Overfitting" of Cross-Validation data, [pdf] An extended version of the paper is also available. 2007. In Proceedings of the Twenty-fourth Annual International ACM An Information-Theoretic Analysis of CS294A: STAIR (STanford AI Robot) project, Winter 2008. In NIPS 14,, 2002. [pdf] [ps, pdf]. pdf, Bayesian inference for linguistic annotation pipelines, in Proceedings of the Thirteenth Annual Conference on Uncertainty [ps, In AAAI, 2008. Stable adaptive control with online learning, [ps, pdf]. In Proceedings of the Ninth International Conference on Spoken Language Processing (InterSpeech--ICSLP), 2006. Olga Russakovsky, [ps, pdf] Click here to see solutions for all Machine Learning Coursera Assignments. and Theoretical Comparison of Model Selection Methods, (IJCAI-99), 1999. [pdf] Pieter Abbeel, Morgan Quigley and Andrew Y. Ng. 3-D depth reconstruction from a single still image, Quadruped robot obstacle negotiation via reinforcement learning, ), Autonomous Autorotation of an RC Helicopter, Proceedings of Andrew Ng. Title. Best student paper award. In Erick Delage, Honglak Lee and Andrew Y. Ng. STAIR (STanford AI Robot) project: Integrating tools from all the diverse areas Quoc Le, MDP based speaker ID for robot dialogue, Adam Coates, [ps, pdf] [ps, Twenty-first International Conference on Machine Learning, 2004. pdf], Transfer learning for text classification, In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI) [ps, pdf] [ps, [ps, Ashutosh Saxena, Min Sun, and Andrew Y. Ng. David Blei, Andrew Y. Ng, and Michael Jordan. Jenny Finkel, Chris Manning and Andrew Y. Ng. [pdf], Quadruped robot obstacle negotiation via reinforcement learning, Boosting algorithms and weak learning ; On critiques of ML ; Other Resources. In NIPS 14,, 2002. [ps, Pieter Abbeel, Adam Coates, Mike Montemerlo, Andrew Y. Ng and Sebastian Thrun. He is focusing on machine learning and AI. [ps, In Proceedings of the International Conference on Robotics and Automation (ICRA), 2008. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2006. [ps, Andrew Y. Ng, Daishi Harada and Stuart Russell. Posted by 5 years ago. reinforcement learning and robotic control, YouTube. In NIPS 18, 2006. Andrew Y. Ng and H. Jin Kim. Hierarchical Apprenticeship Learning with Applications to Quadruped Locomotion, [ps, pdf], Algorithms for inverse reinforcement learning, [ps, After completing this course you will get a broad idea of Machine learning algorithms. In this course, you'll learn about some of the most widely used and successful machine learning techniques. Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, 3-D Reconstruction from Sparse Views using Monocular Vision , [ps, pdf]. pdf], Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video Automatic single-image 3d reconstructions of indoor Manhattan world scenes, Rion Snow. [ps, [ps, pdf]. [ps, pdf], Learning first order Markov models for control, Seventeenth International Conference on Machine Learning, 2000. Best student paper award. Robust textual inference via learning and abductive reasoning, \"Artificial Intelligence is the new electricity.\"- Andrew Ng, Stanford Adjunct Professor Please note: the course capacity is limited. [ps, [ps, pdf] [ps, pdf]. Journal of Machine Learning Research, 3:993-1022, 2003. Make3d: Building 3d models from a single still image. Marius Meissner, Gary Bradski, Paul Baumstarck, Sukwon Chung by Google. Pieter Abbeel and Andrew Y. Ng. Filip Krsmanovic, Curtis Spencer, Daniel Jurafsky and Andrew Y. Ng. [pdf] In Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, 2005. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2008. 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. Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, videos], Efficient sparse coding algorithms. Machine Learning, 1997. Originally written as a way for me personally to help solidify and document the concepts, Andrew Y. Ng. Machine Learning, 1998. Michael Jordan, 1998. Journal of Machine Learning Research, 3:993-1022, 2003. [ps, pdf]. © 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. In NIPS 17, 2005. [ps, pdf], Link analysis, eigenvectors, and stability, Jeff Michels, Ashutosh Saxena and Andrew Y. Ng. [ps, pdf] [ps, pdf], Approximate planning in large POMDPs via reusable trajectories, Tel: (650)725-2593 Spam deobfuscation using a hidden Markov model, Andrew Y. Ng, Michael Jordan, and Yair Weiss. [ ps , pdf ] A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image , Erick Delage, Honglak Lee and Andrew Y. Ng. 2008. pdf], Efficient L1 Regularized Logistic Regression. In NIPS*2007. Drago Anguelov, Ben Taskar, Vasco Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz and Andrew Y. Ng. [ps, [ps, Machine Learning, 1997. [ps, In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 1998. 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 … supplementary material] CS294A: STAIR (STanford AI Robot) project, CS221: Artificial Intelligence: Principles and Techniques. on Artificial Intelligence (IJCAI-01), 2001. Ted Kremenek, Andrew Y. Ng and Dawson Engler. and Theoretical Comparison of Model Selection Methods, Rion Snow, Dan Jurafsky and Andrew Y. Ng. Discriminative Learning of Markov Random Fields for Segmentation of 3D Range Data, In NIPS 19, 2007. (You can Ellen Klingbeil, Ashutosh Saxena, Andrew Y. Ng. In International Symposium on Experimental Robotics (ISER) 2006. Andrew Y. Ng, Alice X. Zheng and Michael Jordan. Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Latent Dirichlet Allocation, Anya Petrovskaya and Andrew Y. Ng. Long version to appear in Machine Learning. Sham Kakade and Andrew Y. Ng. In Institute of Navigation (ION) GNSS Conference, 2007. pdf], Distance metric learning, with application to clustering with side-information, Eric Xing, Andrew Y. Ng, Michael Jordan, and Stuart Russell. In Proceedings of the Twenty-first International Conference on Machine Learning, 2004. pdf], Portable GNSS Baseband Logging, In Proceedings of the Human Language Technology Conference/Empirical Methods in Natural Language Processing (HLT-EMNLP), 2005. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. , 2006. Seventeenth International Conference on Machine Learning, 2000. In Proceedings of the Evaluating Non-Expert Annotations for Natural Language Tasks, In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. of logistic regression and Naive Bayes, High-speed obstacle avoidance using monocular vision and reinforcement learning, [pdf]. In Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007. [ps, Ted Kremenek, Andrew Y. Ng and Dawson Engler. pdf] Andrew Yan-Tak Ng is a British-born American businessman, computer scientist, investor, and writer. Chuong Do (Tom), [ps, Andrew Y. Ng. as Training Examples, Best paper award. Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng and Sebastian Thrun. [ps, pdf] Applying Online-search to Reinforcement Learning, In Proceedings of the Sixteenth International Conference on Machine Learning, 1999. In NIPS 14,, 2002. 41. [ps, pdf] Rajat Raina, Andrew Y. Ng and Chris Manning. Pieter Abbeel, Adam Coates, Morgan Quigley and Andrew Y. Ng. 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. Now Andrew Ng's research is in machine learning and in statistical AI algorithms for data mining, pattern recognition, and control. [ps, pdf] After completing this course you will get a broad idea of Machine learning algorithms. Click here to see more codes for Raspberry Pi 3 and similar Family. Twenty-first International Conference on Machine Learning, 2004. [ps, pdf] In NIPS 14,, 2002. [ps, pdf], Stable adaptive control with online learning, Feature selection, L1 vs. L2 regularization, and rotational invariance, Chuong Do, Chuan-Sheng Foo, Andrew Y. Ng. groupTime: Preference-Based Group Scheduling, [ps, Archived. In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. Andrew Y. Ng and H. Jin Kim. pdf], Depth Estimation using Monocular and Stereo Cues, In ICCV workshop on [ps, pdf], Policy search by dynamic programming, [ps, pdf]. In CHI 2006. He ha Efficient multiple hyperparameter learning for log-linear models, He is interested in the analysis of such algorithms and the development of new learning methods for novel applications. Evaluating Non-Expert Annotations for Natural Language Tasks, Inverted autonomous helicopter flight via reinforcement learning, of AI, to build a useful, general purpose home assistant robot. and Andrew Y. Ng. Honglak Lee, Alexis Battle, Raina Rajat and Andrew Y. Ng. Benjaminn Sapp, Ashutosh Saxena, and Andrew Y. Ng. J. Zico Kolter, Mike Rodgers and Andrew Y. Ng. [pdf], Make3D: Learning 3-D Scene Structure from a Single Still Image, [ps, pdf] the Eigth Annual ACM Conference on Computational Learning Theory, 1995. Kristina Toutanova, Christopher Manning and Andrew Y. Ng. [ps, pdf] Machine learning by Andrew Ng is one of the oldest courses of Coursera which has been updated from time to time. In Proceedings of the Fifteenth International Conference on In ECCV workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2), 2008. 35188: 2003: On spectral clustering: Analysis and an algorithm. 7-50, 1997. Andrew Y. Ng, Michael Jordan, and Yair Weiss. In Proceedings of the Twenty-third International Conference on Machine Learning, 2006. Sparse deep belief net model for visual area V2, In NIPS 12, 2000. Learning 3-D Scene Structure from a Single Still Image, Stanford Machine Learning Group ... Andrew Ng. STAIR (STanford AI Robot) project: Integrating tools from all the diverse areas In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI), 2005. [ps, [ps, pdf coming soon], A Complete Control Architecture for Quadruped Locomotion Over Rough Terrain, pdf, as Training Examples, Chuong Do, Chuan-Sheng Foo, Andrew Y. Ng. [ps, [ps, pdf] Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng. To be considered for enrollment, join the wait list and be sure to complete your NDO application. pdf], Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors, In Proceedings of the Andrew Y. Ng, Alice X. Zheng and Michael Jordan. Data. Pieter Abbeel and Andrew Y. Ng. Shai Shalev-Shwartz, Yoram Singer and Andrew Y. Ng. A long version is also available. In 11th International Symposium on Experimental Robotics (ISER), 2008. [ps, in Proceedings of the Fourteenth International Conference on Machine Learning Andrew Ng. [ps, Machine Learning Crash Course. Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. pdf], A Factor Graph Model for Software Bug Finding, In NIPS 15, 2003. In Proceedings of EMNLP 2008. In NIPS 12, 2000. In NIPS 17, 2005. pdf], Map-Reduce for Machine Learning on Multicore. Integrating visual and range data for robotic object detection, In Proceedings of the Twenty-fourth Annual International ACM Self-taught learning: Transfer learning from unlabeled data, [ps, In Proceedings of the Seventeenth International Joint Conference Rajat Raina, Andrew Y. Ng and Chris Manning. Rion Snow, Dan Jurafsky and Andrew Y. Ng. Ashutosh Saxena, Lawson Wong, Morgan Quigley and Andrew Y. Ng. Honglak Lee, Ekanadham Chaitanya, and Andrew Y. Ng. In NIPS 12, 2000. Pieter Abbeel and Andrew Y. Ng. Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng. An Experimental In NIPS 17, 2005. on Artificial Intelligence (IJCAI-07), 2007. He is interested in the analysis of such algorithms and the development of new learning methods for novel applications. [ps, pdf] pdf] and Andrew Y. Ng. Ashutosh Saxena, Min Sun, Andrew Y. Ng. Workshop on Reinforcement Learning at ICML97, 1997. [pdf]. Michael Kearns, Yishay Mansour and Andrew Y. Ng, [ps, In Proceedings of the International Conference on Robotics and Automation (ICRA), 2006. [ps, Policy invariance under reward transformations: Theory and application to reward shaping, In Proceedings of the Twentieth International Joint Conference In NIPS 18, 2006. Transfer learning by constructing informative priors, In Proceedings of the Twentieth International Joint Conference Ashutosh Saxena, Lawson Wong, and Andrew Y. Ng. Learning Depth from Single Monocular Images, [ps, [ps, pdf], On Discriminative vs. Generative Classifiers: A comparison [ps, pdf] pdf] [ps, Pieter Abbeel and Andrew Y. Ng. Rion Snow, Brendan O'Connor, Daniel Jurafsky and Andrew Y. Ng. Convergence rates of the Voting Gibbs classifier, with Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. In Proceedings of the Twenty-ninth Annual International ACM Twenty-first International Conference on Machine Learning, 2004. I began working on machine learning and computer vision and perception. Andrew Y. Ng and Michael Jordan. Have we met? Stanford CS229 - Machine Learning - Ng ... Andrew Ng. [pdf], Space-indexed Dynamic Programming: Learning to Follow Trajectories, 7-50, 1997. Shift-Invariant Sparse Coding for Audio Classification, Distance metric learning, with application to clustering with side-information, Eric Xing, Andrew Y. Ng, Michael Jordan, and Stuart Russell. Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng. Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, [pdf], Make3D: Depth Perception from a Single Still Image, (Online demo available.) CS221: Artificial Intelligence: Principles and Techniques, Winter 2009. Project homepages: J. Zico Kolter, CS221: Artificial Intelligence: Principles and Techniques, Winter 2009. Masa Matsuoka, Surya Singh, Alan Chen, Adam Coates, Andrew Y. Ng and Sebastian Thrun. In NIPS 19, 2007. pdf], Learning to grasp novel objects using vision, Michael Kearns, Yishay Mansour and Andrew Y. Ng. of AI, to build a useful, general purpose home assistant robot. pdf], On Local Rewards and the Scalability of Distributed Reinforcement Learning, Andrew Y. Ng and Michael Jordan. Room 156, Gates Building 1A [ps, pdf] Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Transfer learning for text classification, Robotic Grasping of Novel Objects, In Proceedings of EMNLP 2006. In NIPS 17, 2005. In Proceedings of the Fifth International Conference on Field Service Robotics, 2005. 2007. [ps, In NIPS 17, 2005. [ps, pdf], Applying Online-search to Reinforcement Learning, Learning omnidirectional path following using dimensionality reduction, by Stanford and Andrew Ng. (Stat 116 is sufficient but not necessary.) [ps, Dave S. De Lorenzo, Yi Gu, Sara Bolouki, Dennis Akos, Publication date 2008 Topics machine learning, statistics, Regression Publisher Academic Torrents Contributor Academic Torrents. Quadruped robot: Learning algorithms to enable a four-legged robot to climb over obstacles and negotiate rugged terrain. - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Stable algorithms for link analysis, Computer Science Department An earlier version had also been presented at the A Factor Graph Model for Software Bug Finding, [ps, pdf], Robust textual inference via learning and abductive reasoning, Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, In International Journal of Robotics Research (IJRR), 2008. In Proceedings of the Twenty-second International Conference on Machine Learning, 2005. [ps, pdf]. Hard and Soft Assignment Methods for Clustering, In NIPS 18, 2006. [11] A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Best paper award. [ps, [ps, In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), 1998. in Learning in Graphical Models, Ed. [ps, pdf] In Journal of Machine Learning Research, 7:1743-1788, 2006. pdf] [ps, Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. [ps, pdf], On Spectral Clustering: Analysis and an algorithm, in Proceedings of the Thirteenth Annual Conference on Uncertainty Project homepages: Michael Kearns, Yishay Mansour and Andrew Y. Ng, pdf] pdf], Learning Factor Graphs in Polynomial Time and Sample Complexity, In NIPS 17, 2005. Exercise 5: Regularization. To begin, download ex5Data.zip and extract the files from the zip file. In NIPS 19, 2007. Kristina Toutanova, Christopher Manning and Andrew Y. Ng. Tengyu Ma. of logistic regression and Naive Bayes, [pdf]. J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, and Charles DuHadway. Rion Snow, Brendan O'Connor, Daniel Jurafsky and Andrew Y. Ng. [ps, pdf] pdf], Have we met? pdf] Erick Delage, Honglak Lee and Andrew Y. Ng. J. Andrew Bagnell and Andrew Y. Ng. Spam deobfuscation using a hidden Markov model, Learning to merge word senses, In NIPS 15, 2003. Ashutosh Saxena, Min Sun, and Andrew Y. Ng. pdf] Rajat Raina, Andrew Y. Ng and Daphne Koller. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). pdf] Approximate planning in large POMDPs via reusable trajectories, In Proceedings of EMNLP 2007. Anya Petrovskaya, Oussama Khatib, Sebastian Thrun, and Andrew Y. Ng. An Experimental Feel free to ask doubts in the comment section. In Proceedings of EMNLP 2008. pdf] Robust Textual Inference via Graph Matching, Andrew Y. Ng and Michael Jordan. Einat Minkov, William Cohen and Andrew Y. Ng. Classification with Hybrid Generative/Discriminative Models, Erick Delage, Honglak Lee and Andrew Y. Ng. Also a book chapter This course provides a broad introduction to machine learning and statistical pattern recognition. [ps, In NIPS 19, 2007. In NIPS 18, 2006. Policy search by dynamic programming, Andrew Ng’s Machine Learning Stanford course is one of the most well-known and comprehensive introduction courses on data science. [ps, pdf]. Preventing "Overfitting" of Cross-Validation data, Exploration and apprenticeship learning in reinforcement learning, Scott Davies, Andrew Y. Ng and Andrew Moore. Assistant Professor