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(Briefly) Active Learning + Course Recap

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Active Learning Remember Problem Set 1 Question #1? – Part (c) required generating a set of examples that would identify the target concept in the worst case. – …we were able to find the correct hypothesis (out of hundreds in H) with only 8 queries! Logarithmic in |X| In general, guaranteeing perfect performance with randomly drawn examples requires a number of queries in |X|. linear

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Active Learning (2) Interesting challenge: choosing which examples are most informative Increasingly important: problems are huge and on-demand labelers are available – “Volunteer armies”: ESP game, Wikipedia – Mechanical Turk Key question: How to identify the most informative queries? – Both a technical question & a human interfaces question

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Recap

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A Few Quotes “A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Chairman, Microsoft) “Machine learning is the next Internet” (Tony Tether, Director, DARPA) “Machine learning is the hot new thing” (John Hennessy, President, Stanford) “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo) “Machine learning is going to result in a real revolution” (Greg Papadopoulos, CTO, Sun) “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo) 5

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Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs 6

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Types of Learning Supervised (inductive) learning – Training data includes desired outputs Unsupervised learning – Training data does not include desired outputs Reinforcement learning – Rewards from sequence of actions Semi-supervised learning – Training data includes a few desired outputs 7

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Supervised Learning GIVEN: Instances X – E.g., days decribed by attributes: Sky, Temp, Humidity, Wind, Water, Forecast Hypothesis space H – E.g. MC2, conjunction of literals: Training examples D – positive and negative examples of the target function c:,…, FIND: A hypothesis h in H such that h(x)=c(x) for all x in D.

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Supervised Learning Algorithms Candidate-Elimination x 1 = x 2 = h 1 = h 2 = h 3 = Instances x2x2 x1x1 Hypotheses h2h2 h3h3 h1h1 h 2 h 1 h 2 h 3 specific general

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Decision Trees Learn conjunction of disjunctions by greedily splitting on “best” attribute values

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Rule Learning Greedily learn rules to cover examples, e.g.: Can also be applied to learn first-order rules:

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Neural Networks Non-linear regression/classification technique Especially useful when inputs/outputs are numeric Long training times, quick testing times Inputs Output Age34 2Gender Stage “Probability of beingAlive” 0.6 .4.2

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Instance Based Methods E.g., K-nearest neighbor Quick training times, long test times The “curse of dimensionality”

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Support Vector Machines (1) Derived Feature Spaces (the Kernel Trick):

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Support Vector Machines (2) Maximizing Margin:

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Bayes Nets (1) Qualitative part: Directed acyclic graph (DAG) Nodes - random vars. Edges - direct influence Quantitative part: Set of conditional probability distributions e b e be b b e BE P(A | B,E) Parents Pa of Alarm Earthquake JohnCalls Burglary Alarm MaryCalls

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Bayes Nets (2) Flexible modeling approach – Used for SL, SSL, UL Natural for explicitly encoding prior knowledge

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Hidden Markov Models Special case of Bayes Nets for sequential data Admit efficient learning, decoding algorithms titi t i+1 t i+2 t i+3 wiwi w i+1 w i+2 w i+3 cities such as Seattle States – unobserved Words – observed

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Computational Learning Theory Based on the data we’ve observed, what can we guarantee? “Probably Approximately Correct” learning Extension to continuous inputs: VC dimension

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Optimization Techniques Local Search – Hill climbing, simulated annealing Genetic Algorithms – Key innovation: crossover – Also applied to programs (genetic programming)

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Unsupervised Learning K-means Hidden Markov Models Both use the same general algorithm… Expectation Maximization

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Key Lessons (1) You can’t learn without inductive bias From the Wired article assigned 1 st week: What do you think? Today companies like Google, which have grown up in an era of massively abundant data, don’t have to settle for wrong models. Indeed, they don’t have to settle for models at all.

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Key Lessons (2) Overfitting – Can’t just choose the “most powerful” model Choose the “right” model – One that encodes your understanding of the domain and meets your other requirements – E.g. HMMs vs. decision trees for sequential data Decision trees vs. NNs for mushrooms NNs vs. decision trees for face recognition

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24 Course Advertisement EECS 395/495 Spring Quarter 2009 “Web Information Retrieval and Extraction” – Basics of Web search, extraction – New research & future directions – Discussion, project based

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Thanks!

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