Physics
568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, FALL
2024
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Instructor:
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Prof. Alexandre V. Morozov Office hour: by request |
Lectures: Tuesday and Thursday, 3.50 - 5.10 pm, SEC-216
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 (09/03)
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Lecture 2 (09/05)
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Lecture 3 (09/10)
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Lecture 4 (09/12)
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Lecture 5 (09/17)
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Lecture 6 (09/19)
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Lecture 7 (09/24)
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Lecture 8 (09/26)
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Lecture 9 (10/01, 10/03)
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Lecture 10 (10/08)
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Lecture 11 (10/10)
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Lecture 12 (10/15)
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Lecture 13 (10/17)
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Lecture 14 (10/22)
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Lecture 15 (10/24)
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Lecture 16 (10/29)
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Lecture 17 (10/31)
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Lecture 19 (11/07, 11/12)
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Lecture 20 (11/14)
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Lecture 21 (11/19)
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Lecture 22 (11/21)
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Lecture 23 (11/26)
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Lecture 24 (12/03)
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Lecture 25 (12/10)
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Homework 1 (due 09/19):
Problems
Solutions
Homework 2 (due 10/10):
Problems
Solutions
Homework 3 (due 11/05):
Problems
Solutions
Homework 4 (due 12/05):
Problems
Please
send any comments
about this page to morozov at physics.rutgers.edu
Textbooks:
Probabilistic Machine Learning: An Introduction by Kevin P. Murphy.
Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy.
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 12/18 by midnight):
Department of Physics
and
Astronomy Main Page