Physics
568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, SPRING
2023
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Instructor:
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Prof. Alexandre V. Morozov Office hour: by request |
Lectures: Tuesday and Friday, 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 project (72 hours, open book, open notes).
The grade is
determined according to the following formula: total score = 1/2(homework) + 1/2(final) Lecture 1 (01/17)
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Lecture 2 (01/20)
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Lecture 3 (01/24)
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Lecture 4 (01/27)
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Lecture 5 (01/31 and 02/03)
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Lecture 6 (02/07)
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Lecture 7 (02/10)
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Lecture 8 (02/14)
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Lecture 9 (02/17)
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Lecture 10 (02/21)
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Lecture 11 (02/24)
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Lecture 12 (02/28 and 03/03)
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Lecture 13 (03/07 and 03/10)
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Lecture 14 (03/21)
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Lecture 14 Supplement
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Lecture 15 (03/24 and 03/28)
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Lecture 16 (03/31)
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Lecture 17 (04/04)
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Lecture 18 (04/07)
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Lecture 19 (04/18)
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Lecture 20 (04/21)
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Lecture 21 (04/25)
CNN lecture
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Lecture 22 (04/28)
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Hinton & Salakhutdinov Science 2006
SI
Homework 1 (due 02/10):
Problems
Homework 2 (due 03/03):
Problems
Homework 3 (due 03/31):
Problems
Homework 4 (due 04/28):
Problems
Note: NO lecture on Friday, Feb. 17
Note: NO lectures on Tuesday, Apr. 11 and Friday, Apr. 14: STRIKE!
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 05/05 by 5 pm):
Department of Physics
and
Astronomy Main Page