Physics
568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, SPRING
2020
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Instructor:
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Prof. Alexandre V. Morozov Office hour: by request |
Lectures: Monday and Thursday, 10.20 - 11.40 am, SEC-204
Prerequisites: Basic knowledge of linear algebra and probability theory. Homework and Exam: One homework per 2-3 weeks. There will be a final take-home exam/project (72 hours, open book, open notes).
The grade is
determined according to the following formula: total score = 1/2(final) + 1/2(homework) Lecture 1 (01/23)
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Lecture 2 (01/27)
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Lecture 3 (01/30)
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Lecture 4 (02/03)
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Lecture 5 (02/06 and 02/10)
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Lecture 6 (02/13)
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Lecture 7 (02/17)
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Lecture 8 (02/20)
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Lecture 9 (02/24)
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Lecture 10 (02/27)
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Lecture 11 (03/02)
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Lecture 12 (03/05)
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Lecture 13 (03/09 and 03/12)
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Lecture 14 (03/23)
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Lecture 14 Supplement (03/29)
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Lecture 15 (03/26)
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Lecture 16 (03/30)
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Lecture 17 (04/02)
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Lecture 18 (04/06)
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Lecture 19 (04/09)
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[based on Mehta et al., Section IV (pp. 13-19)]
Lecture 20 (04/13)
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Lecture 21 (04/16)
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Lecture 22 (04/20)
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Lecture 23 (04/23)
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Lecture 24 (04/27)
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[based on MacKay 42.1-42.5 (pp. 505-510)]
Lecture 25 (04/30)
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[based on MacKay 42.7-42.8 (pp. 512-516) and 43.1 (pp. 522-525)]
Amit et al PRL 1985
Lecture 26 (05/04)
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[based on MacKay 43.1-43.2 (pp. 522-526)]
Hinton & Salakhutdinov Science 2006
SI
Homework 1 (due 02/13):
Problems
Homework 2 (due 03/09):
Problems
Homework 3 (due 03/23):
Problems
Homework 4 (due 04/13):
Problems
Datasets
RVM software
Homework 5 (due 04/27):
Problems
Datasets
Please
send any comments
about this page to morozov at physics.rutgers.edu
Textbooks:
Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop.
Information Theory, Inference and Learning Algorithms by David J. C. MacKay.
Reviews:
Introduction to Machine Learning for physicists by Pankaj Mehta et al.
Lecture Notes:
Homework:
Final Exam (due 05/08 by 10 am):
Department of Physics
and
Astronomy Main Page