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README.md

M1399.000200 Advanced Statistical Computing @ SNU 2020

This is the course website for M1399.000200: "Advanced Statistical Computing " at Seoul National University in Fall 2020. Assignments, lecture notes, and open source code will all be available on this website.

Announcements

  • 2020-12-02: Homework 4, Q2-3 has been clarified.
  • 2020-11-22: Homework 4 has been posted. Due data is 2020-12-14.
  • 2020-11-18: GP lecture notes have been updated to include the ECOS solver.
  • 2020-11-10: Homework 3 Q3-2 has been corrected.
  • 2020-10-31: Homework 3 has been posted. Due date is 2020-11-17.
  • 2020-10-14: Final project has been announced.
  • 2020-10-12: Homework 2 typos has been fixed. See especially Q5.
  • 2020-09-27: Homework 2 has been posted. Due date is 2020-10-18.
  • 2020-09-27: A make-up lecture for 2020-09-30 class will be recorded and posted on eTL.
  • 2020-09-27: Lecture note 3 has been updated.
  • 2020-09-16: Fixed typos in Homework 1.
  • 2020-09-11: Homework 1 has been posted. Due date is 2020-09-27.
  • 2020-09-02: 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:

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 in Julia by yourself. 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.

Week Topic Assignment Due Date
1 (9/2) Introduction, Julia Intro [notebook]
2 (9/7, 9/9) Plotting [notebook], Jupyter Homework 1 [notebook] 2020-09-27
3 (9/14, 9/16) Computer Arithmetic [notebook]
4 (9/21, 9/23) Computer Arithmetic [notebook], Algorithm [notebook]
5 (9/28, 9/30) Numerical Linear Algebra: intro [notebook], triangular systems [notebook] Homework 2 [notebook] 2020-10-18
6 (10/5, 10/7) LU decomposition [notebook] [example], Cholesky [notebook]
7 (10/12, 10/14) QR decomposition [notebook], Linear regression [notebook], Iterative methods [notebook] Final Project Proposal 2020-10-26
8 (10/19, 10/21) Eigenvalue and singular value decompositions [notebook]
9 (10/25, 10/28) Introduction to mathematical optimization Homework 3 [notebook] 2020-11-17
10 (11/2, 11/4) Optimization in Julia [notebook]
11 (11/9, 11/11) Linear programming [notebook], Quadratic programming [notebook], Second-order cone programming [notebook]
12 (11/16, 11/18) Semidefinite programming [notebook], Geometric programming [notebook]
13 (11/23, 11/25) KKT conditions, Newton's method I [notebook] Homework 4 [notebook] 2020-12-14
14 (11/30, 12/2) Newton's method II, MM algorithms
15 (12/7, 12/9) First-order methods, Final Projects
16 (12/14) Final Projects

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Advanced Statistical Computing @ SNU 2020

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