![]() Use binScatterPlot to examine the relationship between the Hr and DepDelay variables.ĭepDelay ~ 1 + Year + Month + DayofMonth + DayOfWeek + DepTime + ArrDelay + Distance + Hr The visualizations all trigger execution, similar to calling the gather function. To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the mapreducer function. When you perform calculations on tall arrays, MATLAB® uses either a parallel pool (default if you have Parallel Computing Toolbox™) or the local MATLAB session. The fundamental difference is that tall arrays typically remain unevaluated until you request that the calculations be performed. Instead of writing specialized code that takes into account the huge size of the data, such as with MapReduce, you can use tall arrays to work with large data sets in a manner similar to in-memory MATLAB arrays. This type of data consists of a very large number of rows (observations) compared to a smaller number of columns (variables). Tall arrays and tables are designed for working with out-of-memory data. 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.This example shows how to perform statistical analysis and machine learning on out-of-memory data with MATLAB® and Statistics and Machine Learning Toolbox™. 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. 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. ![]() 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. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" 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. Ng's research is in the areas of machine learning and artificial intelligence. Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.) (Stat 116 is sufficient but not necessary.) Familiarity with the basic probability theory. Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Students are expected to have the following background: 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. ![]() Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines) unsupervised learning (clustering,ĭimensionality reduction, kernel methods) learning theory (bias/variance tradeoffs VC theory large margins) reinforcement learning and adaptive control. ![]() This course provides a broad introduction to machine learning and statistical pattern recognition.
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