Advanced Topics in Machine Learning - Caltech

More documents

Recommendations

Info

Key ques8ons ! How can we deal with data sets that don’t fit in main memory of a single machine? Online learning ! Labels are expensive. How can we obtain most informa8ve labels at minimum cost? Ac=ve learning ! How can we adapt complexity of classifiers for large data sets? Nonparametric learning 10
Overview ! Research-‐oriented advanced topics course ! 3 main topics ! Online learning (from streaming data) ! Ac8ve learning (for gathering most useful labels) ! Nonparametric learning (for model selec8on) ! Both theory and applica8ons ! Handouts etc. on course webpage ! h?p://www.cs.caltech.edu/courses/cs253/ 11
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: How can we get gain insight f
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 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?