Advanced Topics in Machine Learning - Caltech

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Comparison Passive learning Ac8ve learning Labels needed to learn with classifica8on error ε Ω(1/ε) O(log 1/ε) Ac=ve learning can exponen=ally reduce the number of required labels! 36
Key ques8ons ! For which classifica8on tasks can we provably reduce the number of labels? ! Can we do worse by ac8ve learning? ! Can we implement ac8ve learning efficiently? 37
Page 1 and 2: Advanced Topics in Machine Learn
Page 3 and 4: Web-‐scale machine learning !
Page 5 and 6: Monitoring transients in astrono
Page 7 and 8: How is the data-‐rich scien
Page 9 and 10: How can we get gain insight f
Page 11 and 12: Overview ! Research-‐oriented
Page 13 and 14: Background & Prequisites ! Formal
Page 15 and 16: Course project ! “Get your han
Page 17 and 18: Course overview ! Online learning
Page 19 and 20: Main memory vs. disk access Mai
Page 21 and 22: Model: Predic8on from expert ad
Page 23 and 24: Expert selec8on strategies ! Pick
Page 25 and 26: Guarantees for RWM Theorem For a
Page 27 and 28: Course overview ! Online learning
Page 29 and 30: Learning binary thresholds ! Inpu
Page 31 and 32: ! Input domain: D=[0,1] Ac8ve le
Page 33 and 34: Sta8s8cal ac8ve learning protoco
Page 35: Label complexity for ac8ve lear
Page 39 and 40: Nonlinear classifica8on -----+ + -
Page 41 and 42: Nonlinear classifica8on +-- + +--
Page 43 and 44: From linear to nonlinear classi
Page 45 and 46: Representa8on of func8on f Solu8
Page 47 and 48: Nonparametric solu8on Solu8on of
Page 49: Course overview Online Learning Re

Advanced Topics in Machine Learning - Caltech

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