Physics 568: LARGE SCALE DATA ANALYSIS IN PHYSICS & ASTRONOMY, SPRING 2023

 


Instructor:

 

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

Office hour:  by request   




Lectures:
Tuesday and Friday, 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 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 Notes:

Lecture 1 (01/17)   pdf

Lecture 2 (01/20)   pdf

Lecture 3 (01/24)   pdf

Lecture 4 (01/27)   pdf

Lecture 5 (01/31 and 02/03)   pdf

Lecture 6 (02/07)   pdf

Lecture 7 (02/10)   pdf

Lecture 8 (02/14)   pdf

Lecture 9 (02/17)   pdf

Lecture 10 (02/21)   pdf

Lecture 11 (02/24)   pdf

Lecture 12 (02/28 and 03/03)   pdf

Lecture 13 (03/07 and 03/10)   pdf

Lecture 14 (03/21)   pdf   Lecture 14 Supplement   pdf

Lecture 15 (03/24 and 03/28)   pdf

Lecture 16 (03/31)   pdf

Lecture 17 (04/04)   pdf

Lecture 18 (04/07)   pdf

Lecture 19 (04/18)   pdf

Lecture 20 (04/21)   pdf

Lecture 21 (04/25)   CNN lecture   pdf

Lecture 22 (04/28)   pdf   Hinton & Salakhutdinov Science 2006   SI



Homework:

Homework 1 (due 02/10): Problems

Homework 2 (due 03/03): Problems

Homework 3 (due 03/31): Problems

Homework 4 (due 04/28): Problems



Final Exam (due 05/05 by 5 pm):

Problems   Datasets



Note: NO lecture on Friday, Feb. 17

Note: NO lectures on Tuesday, Apr. 11 and Friday, Apr. 14: STRIKE!






Students with Disabilities


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

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