 | Lecture 12 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 14 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 17 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 10 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 20 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 19 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Lecture 7 | Machine Learning (Stanford) Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Statistical Aspects of Data Mining (Stats 202) Day 1 Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease |
 | Lecture 27 | Programming Methodology (Stanford) Lecture by Professor Mehran Sahami for the Stanford Computer Science Department (CS106A). Professor Sahami lectures on options and opportunities after his class. He shows students the path of majoring in CS and explains what each class will offer. CS106A is an Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=84A56BC7F4A1F852 CS106A at Stanford Unversity: http://www.stanford.edu/class/cs106a/ Stanford Center for Professional Development: http://scpd.stanford.edu/ Stanford University: http://www.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford |
 | Learning 3D Models from a Single Still Image Google Tech Talks January, 29 2008 ABSTRACT We present an algorithm to convert standard digital pictures into 3-d models. This is a challenging problem, since an image is formed by a projection of the 3-d scene onto two dimensions, thus losing the depth information. We take a supervised learning approach to this problem, and use a Markov Random Field (MRF) to model the image depth cues as well as the relationships between different parts of the image. We show that even on unstructured scenes (of indoor and outdoor environments, including forests, trees, buildings, etc.), our algorithm is frequently able to recover fairly accurate 3-d models. We use our method to create visually pleasing 3-d flythroughs from the image. We also present a few extensions of these ideas, such as additionally incorporating triangulation (stereo) cues, and using multiple images to produce large scale 3-d models. We also apply our methods to two robotics applications: (a) high speed offroad obstacle avoidance on an autonomously driven remote-controlled car, and (b) having a robot unload items from a dishwasher. To convert your own image of an outdoor scene, landscape, etc. to a 3-d model, please visit: http://make3d.stanford.edu Joint work with Min Sun and Andrew Y. Ng. Speaker: Ashutosh Saxena Ashutosh is a PhD candidate with Prof. Andrew Y. Ng in the Computer Science department in Stanford University. He received his B. Tech. from Indian Institute of Technology (IIT Kanpur) in 2004. His research focuses on machine learning approaches to problems in computer vision and in robotic manipulation. Using data-driven machine learning techniques, he developed algorithms for creating 3-d models from a single image, and algorithms for robotic manipulation tasks such as opening doors, and grasping previously unseen objects. |
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