Python basics I (installation, variables, list, loops)
2
Python basics II (function, advanced libraries)
3
Integrals/derivatives
4
Fitting/interpolation
5
Fourier transform
6
Random numbers
7
Monte carlo
8
X-ray diffraction and object oriented programming
9
Optmization I
10
Optmization II
11
Optmization III
12
Machine Learning I (regression, clustering)
13
Machine Learning II (applications)
14
Machine Learning III (database)
Prerequisites: PHYS 152, PHYS 152L or PHYS 180
Credit Hours: 3
Textbook: Computational Physics, M. Newman (not required)
Grade Distribution:
Items
Percentage
Attendance
10%
Problems and Quiz
20%
Projects
40%
Final Exam (oral)
30%
Course Description
This course is open to all students who are interested in scientific programming and data analysis. It will teach students to write programs to solve simple physics programs on the computer and to manage their codes via github. There will be weekly assignments and two projects during the semester, plus an oral exam in the end of semester. Please bring your laptop to class. All the practices will be based on Python 3. Barring documentable emergencies or observance of a certifable regious holiday, all exams must be taken at the time and place specified.
Appendix
In addtion to the code page, we also have a wiki page which has extended discussions on some focused topics. Most of them were created by the students.
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