Ng, Andrew
Ng's research is in the areas of machine learning and artificial intelligence. 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. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. 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.
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. 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. 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.
info.pdf | Course Information |
schedule.pdf | Course Schedule |
AI-classes.pdf | Other AI Courses |
cs229-notes1.pdf | Linear Regression, Classification and logistic regression, Generalized Linear Models |
cs229-notes2.pdf | Generative Learning algorithms |
cs229-notes3.pdf | Support Vector Machines |
cs229-notes4.pdf | Learning Theory |
cs229-notes5.pdf | Regularization and model selection |
cs229-notes6.pdf | The perceptron and large margin classifiers |
cs229-notes7a.pdf | The k-means clustering algorithm |
cs229-notes7b.pdf | Mixtures of Gaussians and the EM algorithm |
cs229-notes8.pdf | The EM algorithm |
cs229-notes9.pdf | Factor analysis |
cs229-notes10.pdf | Principal components analysis |
cs229-notes11.pdf | Independent Components Analysis |
cs229-notes12.pdf | Reinforcement Learning and Control |
Linear Algebra Review and Reference | cs229-linalg.pdf | ||||
Probability Theory Review | cs229-prob.pdf | ||||
Matlab Review |
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Convex Optimization Overview, Part I | cs229-cvxopt.pdf | ||||
Convex Optimization Overview, Part II | cs229-cvxopt2.pdf | ||||
Hidden Markov Models | cs229-hmm.pdf | ||||
Gaussian Processes |
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Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. |
Previous projects: A list of last year's final projects can be found here. |
Matlab Resources Here are a couple of Matlab tutorials that you might find helpful: Matlab Tutorial and A Practical Introduction to Matlab. For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, and a helpful emac's file. |
Octave Resources For a free alternative to Matlab, check out GNU Octave. The official documentation is available here. Some useful tutorials on Octave include Octave Tutorial and Octave on Wiki. |
Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one. |
Assignment | Assignment Data Files | Solution | Solution Data Files |
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Problem Set 1 | PS1-data.zip | Solution Set 1 | ps1_solution-data.zip |
Problem Set 2 | PS2-data.zip | Solution Set 2 | |
Problem Set 3 | PS3-data.zip | Solution Set 3 | ps3_solution-data.zip |
Problem Set 4 | PS4-data.zip | Solution Set 4 | ps4_solution-data.zip |
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Duration: | |
Watch Now | Download | 1 hr 9 min | |
Topics: The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning |
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Watch Now | Download | 1 hr 16 min | |
Topics: An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations |
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Watch Now | Download | 1 hr 13 min | |
Topics: The Concept of Underfitting and Overfitting, The Concept of Parametric Algorithms and Non-parametric Algorithms, Locally Weighted Regression, The Probabilistic Interpretation of Linear Regression, The motivation of Logistic Regression, Logistic Regression, Perceptron |
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Duration: | |
Watch Now | Download | 1 hr 14 min | |
Topics: Multinomial Event Model, Non-linear Classifiers, Neural Network, Applications of Neural Network, Intuitions about Support Vector Machine (SVM), Notation for SVM, Functional and Geometric Margins |
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Duration: | |
Watch Now | Download | 1 hr 22 min | |
Topics: Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example - Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem |
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Duration: | |
Watch Now | Download | 1 hr 17 min | |
Topics: Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA |
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Watch Now | Download | 1 hr 17 min | |
Topics: State-action Rewards, Finite Horizon MDPs, The Concept of Dynamical Systems, Examples of Dynamical Models, Linear Quadratic Regulation (LQR), Linearizing a Non-Linear Model, Computing Rewards, Riccati Equation |
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Duration: | |
Watch Now | Download | 1 hr 16 min | |
Topics: Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG) |