M1399.000200 Advanced Statistical Computing @ SNU 2021
This is the course website for M1399.000200: “Advanced Statistical Computing “ at Seoul National University in Fall 2021. Assignments, lecture notes, and open source code will all be available on this website.
Announcements
- 2021-11-21: HW3 has been posted. Due is 2021-12-13.
- 2021-11-13: HW3 Q5 instruction has changed.
- 2021-11-01: HW3 has been posted. Due is 2021-11-16.
- 2021-10-16: Lecture notes on eigenvalue decomposition has been updated. Please downloaded the latest version.
- 2021-10-17: Project instruction has been posted. Proposal due is
2021-10-252021-10-28. - 2021-09-25: Minor errors in lecture notes on Computer Arithmetic has been fixed. Thanks Taeyoung!
- 2021-09-24: HW2 has been posted. Due is 2021-10-21.
- 2021-09-23: HW1, Q4 has been clarified. Use the update notebook.
- 2021-09-22: Lecture notes on Computer Arithmetic and Algorithms have been updated. If you downloaded the notes before, try it again.
- 2021-09-08: Homework 1 has been posted.
- 2021-09-07: R package microbenchmark has been installed to the interactive lecture note
Julia Intro 1. Now you can run all the cells online. - 2021-09-01: course will be given online. Mostly real-time, but sometimes pre-recorded.
Instructor
Joong-Ho (Johann) Won
Email: wonj AT stats DOT snu DOT ac DOT kr
Class Time: Mondays/Wednesdays 11:00 - 12:15 @ online
Office Hours: By appointment.
Textbook: There is no required textbook.
References:
- James Gentle, Computational Statistics, 2nd Edition, Springer (2009).
- Gene Golub and Charles Van Loan, Matrix Computation, 4th Edition, Johns Hopkins Press (2012).
- Kenneth Lange, Numerical Analysis for Statisticians, 2nd Edition, Springer (2010).
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press (2004).
- Dimitri P. Bertsekas, Convex Optimization Theory Athena Scientific (2009).
Course Objectives
By the end of this course, you will be able to acquire
- basic programming skills using the Julia programming language;
- basic knowledge of computer arithmetic;
- fundamental knowledge of numerical algorithms for statistical computing;
- hands-on knowledge of various optimization problems in statistical computing;
- basic theoretical understanding of mathematical optimization;
- wisdom of how not to reinvent the wheel.
Course Overview
Assessment
The course will be graded based on the following components:
- Attendance (10%): Mandatory.
- Assignments (65%): You will be assigned 4 homework assignments to be completed using Julia regularly throughout class.
- Final project (25%): The project will be a reproduction of the code and results in a recent computational statistics research paper chosen by yourself in Julia . The ideas for projects will be provided towards the midpoint of the semester.
Schedule
The following schedule is tentative, and is subject to change over the course.
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