Advanced Topics in Machine Learning - Caltech

More documents

Recommendations

Info

Prac8cal problems ! In many applica8ons, number of experts (classifiers) is infinite Online op8miza8on (e.g., online convex programming) ! Ozen, only par8al feedback is available (e.g., obtain loss only for chosen classifier) Mul8-‐armed bandits, sequen8al experimental design ! Many prac8cal problems are high-‐dimensional Dimension reduc8on, sketching 26
Course overview ! Online learning from massive data sets ! Ac=ve learning to gather most informa8ve labels ! Nonparametric learning to adapt model complexity This lecture: Quick overview over all these topics 27
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: Guarantees for RWM Theorem For a
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 and 36: Label complexity for ac8ve lear
Page 37 and 38: Key ques8ons ! For which classif
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?