Physics
568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, FALL
2026
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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, ARC-107
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)
pdf
Lecture 2 (09/08, 09/10)
pdf
Lecture 3 (09/14)
pdf
Lecture 4 (09/17)
pdf
Homework 1 (due 09/21):
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:
Department of Physics
and
Astronomy Main Page