Physics 568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, SPRING 2020

 


Instructor:

 

Prof. Alexandre V. Morozov
Office: Serin E266
E-mail: morozov AT physics.rutgers.edu
Phone:  (848) 445-1387

Office hour:  by request   




Lectures:
Monday and Thursday, 10.20 - 11.40 am, SEC-204


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.  


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 Notes:

Lecture 1 (01/23)   pdf

Lecture 2 (01/27)   pdf

Lecture 3 (01/30)   pdf

Lecture 4 (02/03)   pdf

Lecture 5 (02/06 and 02/10)   pdf

Lecture 6 (02/13)   pdf

Lecture 7 (02/17)   pdf

Lecture 8 (02/20)   pdf

Lecture 9 (02/24)   pdf

Lecture 10 (02/27)   pdf

Lecture 11 (03/02)   pdf

Lecture 12 (03/05)   pdf

Lecture 13 (03/09 and 03/12)   pdf

Lecture 14 (03/23)   pdf   Lecture 14 Supplement (03/29)   pdf

Lecture 15 (03/26)   pdf

Lecture 16 (03/30)   pdf

Lecture 17 (04/02)   pdf



Homework:

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






Students with Disabilities


Please send any comments about this page to morozov at physics.rutgers.edu

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