lecture1-annotated - Machine Learning 10-701/15-781 Fall...

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1 © Eric Xing @ CMU, 2006-2008 Machine Learning Machine Learning 10 10 -701/15 701/15 -781, 781, Fall Fall 2008 2008 Introduction Introduction and and Density Estimation Density Estimation Eric Xing Eric Xing Lecture 1, September 8, 2008 Reading: Chap. 1,2, CB © Eric Xing @ CMU, 2006-2008 Class Registration z IF YOU ARE ON THE WAITING LIST : This class is now fully subscribed. You may want to consider the following options: w Take the class when it is offered again in the spring semester; w Come to the first several lectures and see how the course develops. We will admit as many students from the waitlist as we can, once we see how many registered students drop the course during the first two weeks.
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2 © Eric Xing @ CMU, 2006-2008 z Class webpage: z http://www.cs.cmu.edu/~epxing/Class/10701/ Machine Learning 10-701/15-781 © Eric Xing @ CMU, 2006-2008 Logistics z Text book z Chris Bishop, Pattern Recognition and Machine Learning (required) z Tom Mitchell, Machine Learning z David Mackay, Information Theory, Inference, and Learning Algorithms z Mailing Lists: z To contact the instructors: [email protected] z Class announcements list: [email protected] z TA: z J erry Fu , Doherty Hall 4302E, x8-2039, Office hours: 12-1pm Thu z Mark Palatucci , Smith Hall 232.05, x8-2259, Office hours: 1-2pm Wed z Suyash Shringarpure , Doherty Hall 4301A, x8-1845, Office hours: 3-4pm Wed z Hanghang Tong , Wean Hall 5117, x8-3046, Office hours: 4-5pm Thu z Class Assistant: z Michelle Martin , Wean Hall 4619, x8-5527 z Diane Stidle , Wean Hall 4612, x8-1299
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3 © Eric Xing @ CMU, 2006-2008 Logistics z 5 homework assignments: 30% of grade z Theory exercises z Implementation exercises z Final project: 20% of grade z Applying machine learning to your research area z NLP, IR, Computational biology, vision, robotics … z Theoretical and/or algorithmic work z a more efficient approximate inference algorithm z a new sampling scheme for a non-trivial model … z 3-stage reports z Two exams: 25% of grade each z Theory exercises and/or analysis z Policies … © Eric Xing @ CMU, 2006-2008 Apoptosis + Medicine What is Learning Grammatical rules Manufacturing procedures Natural laws Inference Learning is about seeking a predictive and/or executable understanding of natural/artificial subjects, phenomena, or activities from …
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4 © Eric Xing @ CMU, 2006-2008 Learning tasks … Speech recognition Information retrieval Information retrieval vision control control Planning Planning Games Games Evolution Pedigree Pedigree © Eric Xing @ CMU, 2006-2008 Machine Learning
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5 © Eric Xing @ CMU, 2006-2008 Fetching a stapler from inside an office --- the Stanford STAIR robot © Eric Xing @ CMU, 2006-2008 Machine Learning Machine Learning seeks to develop theories and computer systems for z representing; z classifying, clustering and recognizing; z reasoning under uncertainty; z predicting; z and reacting to z complex, real world information, based on the system's own experience with data , and (hopefully) under a explicit model or mathematical framework , that z can be formally characterized and analyzed z can take into account human prior knowledge z can generalize and adapt across data and domains z can operate automatically and autonomously z and can be interpreted and perceived by human .
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lecture1-annotated - Machine Learning 10-701/15-781 Fall...

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