mlintro[1] - Machine Learning Intro CPS 170 Ron Parr...

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Unformatted text preview: Machine Learning Intro CPS 170 Ron Parr Why Study Learning? •  Considered a hallmark of intelligence •  Viewed as way to reduce programming burden •  Many algorithms assume parameters that are difficult to determine exactly a priori 1 Examples •  SPAM classificaJon •  ComputaJonal Biology/medicine –  DisJnguish healthy/diseased Jssue (e.g., skin/colon cancer) –  Find structure in biological data (regulatory pathways) •  Financial events –  Predict good/bad credit risks –  Predict price changes –  Response to markeJng •  •  •  •  Drilling sites likely to have oil Document categorizaJon Learn to play games Learn to control systems –  Fly Helicopter –  OpJmize OS components •  Public database of learning problems: –  hYp:// Who Does Machine Learning? •  In AI –  Core AI topic (AAAI, IJCAI) –  Specialized communiJes (ICML, NIPS) •  Databases (data mining  ­ KDD) •  Used in (CS): –  Vision –  Systems –  Comp. Bio •  StaJsJcs 2 Who Does Machine Learning (@Duke) •  CS: –  Faculty: Pankaj Agarwal, Vince Conitzer, Alex Hartemink, Kamesh Munagala, Ron Parr, Carlo Tomasi, Jun Yang •  ISDS (everybody, but especially): –  ScoY Schmidler, Sayan Mukherjee •  IGSP: –  Terry Furey, Uwe Ohler •  Engineering: –  Larry Carin, Silvia Ferrari, Rebecca WilleY Who Hires in Machine Learning? •  •  •  •  •  •  UniversiJes Microsoe Research Search: Google/Yahoo/Amazon Defense contractors Some financial insJtuJons (quietly) Many startups •  ML viewed as good background for many other tasks (roboJcs, vision, systems, engineering) 3 What is Machine Learning •  Learning Element –  The thing that learns •  Performance Element –  ObjecJve measure of progress •  Learning is simply an increase in the ability of the learning element over Jme to achieve the task specified by the performance element ML vs. StaJsJcs? •  Machine learning is: –  –  –  –  –  Younger More empirical More algorithmic (arguably) More pracJcal (arguably) More decision theoreJc •  StaJsJcs is: –  More mature –  (arguably) More formal and rigorous 4 ML vs. Data Mining •  Machine Learning is: –  (Arguably) more formal –  (Arguably) more task driven/decision theoreJc •  Data Mining is: –  More constrained by size of data set –  More closely Jed to database techniques Types of Learning •  InducJve Learning –  Acquiring new informaJon that previously was not available –  Learning concepts •  Speedup learning –  Learning to do something you already “know” faster or beYer 5 Feedback in Learning •  Supervised Learning –  Given examples of correct behavior •  Unsupervised Learning –  No external noJon of what is correct –  Is this well ­defined? •  Reinforcement Learning –  Indirect indicaJon of effecJveness Learning Methodology •  DisJncJon between training and tesJng is crucial •  Correct performance on training set is just memorizaJon! •  Researcher should never look at the test data •  Raises some troubling issues for “benchmark” learning problems 6 ComputaJonal Learning Theory •  Formal study of what can be learned from data •  Closely related to ML, but also to CS theory •  AssumpJons: –  Training examples must be representaJve –  Algorithm needn’t always work, but should scale well •  Goals: –  Algorithms that have a low error rate with high probability –  Good characterizaJon of how performance scales COLT •  Learning theory is elegant and mathemaJcally rich. However, –  It someJmes isn’t construcJve –  It someJmes tells us how many data are needed, but not how to manipulate the data efficiently •  Through the late 90’s, learning theory drieed away from pracJcal learning algorithms •  New advances fresh thinking have led to a rapprochement, e.g.: –  Support vector machines –  BoosJng 7 Example: Supervised Learning •  Classical framework •  Target concept, e.g., green •  Learner is presented with labeled instances –  True: Green cones, green cubes, green spheres –  False: Red cones, red cubes, red spheres, blue cones, blue cubes, blue spheres •  Learner must correctly idenJfy the target concept from the training data Performance Measure •  Training set won’t have all possible objects •  Test set will contain novel objects –  Blue cylinders, yellow tetrahedra •  To learn successfully, learner must have good performance when confronted w/novel objects –  This is what we would expect from people –  A blue Broccolisaurus is sJll blue 8 Why Learning Is Tricky •  Suppose we have seen: –  Red tetrahedron(f), Blue sphere(t), Blue cone (t), green cube(f) •  Possible concepts: –  Blue –  (Blue Sphere) or (Blue Cone) –  Objects a prime number from start –  Objects with a circular cross ­secJon •  What if some data are mislabeled? Learning and RepresentaJon •  Learning is very sensiJve to representaJon •  Every learning algorithm can be viewed as a search through a space of concepts •  Space of concepts determines –  –  –  –  Difficulty of task Appropriate algorithm RestricJng too aggressively can trivialize problem Failure to restrict (or regularize) can trivialize the problem •  Example Space: ConjuncJons of colors and shapes –  Eliminates primes and (possibly) cross secJons 9 Management of the Hypothesis Space •  Ockham’s Razor: –  All things being equal, favor the simplest consistent hypothesis –  Guiding principle of science,e.g., Einstein: ‘In my opinion the theory here is the logically simplest relaJvisJc field theory that is at all possible. But this does not mean that nature might not obey a more complex theory. More complex theories have frequently been proposed… In my view, such more complicated systems and their combinaJons should be considered only if there exist physical ­empirical reasons to do so.’ •  Ockham’s razor is not provably correct, but –  ComputaJonal learning theory shows us that the more choices we have, the more data we need to disJnguish reliably among these choices –  Well known trade off between bias and variance •  How many points do you need to fit a degree 2 polynomial? •  How many points do you need to fit a degree 100 polynomial? •  Ockham’s razor is embodied in a wide range of methods Learning Intro Final Thoughts •  Machine learning is one of the most successful areas of AI –  Many pracJcal applicaJons –  Many ways to succeed without solving the “whole problem” –  Many fields view machine learning as a special sauce that will give them an advantage •  Machine learning conferences are almost as large as the general AI conferences 10 How to Succeed with Machine Learning •  TheoreJcal/algorithmic success –  Maneuver through space of hypotheses efficiently –  Efficiency •  Make good use of data •  Make good use of Jme •  PracJcal Success –  Gepng something to learn can be hard (my job!) –  Know your problem! •  Pick training data carefully •  Crae hypothesis space 11 ...
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This note was uploaded on 02/17/2012 for the course COMPSCI 170 taught by Professor Parr during the Spring '11 term at Duke.

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