Math 390R/490R - Special topics in Mathematics/Math seminar - Winter 2025

Class Information

Instructor: Tuan Pham
Class meetings: M, W, F: 10 - 10:50 AM at SCB 304
[Class schedule]   [390R Syllabus]   [490R Syllabus]
[390R Canvas]   [490R Canvas]  

Office Hours

Monday, Wednesday, Friday: 11:00 AM - 12:30 PM at SCB 316, or by appointment

Assignments

  • Homework problems are to be submitted at the beginning of the class.
  • Labs are to be submitted on Canvas as an ipynb file. See the instruction for setup here.
  • Quizzes are given in class. See the class schedule above.
    Homework Quizzes Labs
    Homework 1 Quiz 1 Lab 1: ipynb, pdf
    Homework 2 Quiz 2 Lab 2: ipynb, pdf
    Homework 3 Quiz 3 Lab 3: ipynb, pdf
    Homework 4 Quiz 4 Lab 4: ipynb, pdf
    Homework 5 Quiz 5 Lab 5: ipynb, pdf
    Homework 6
    Homework 7
    Homework 8

    • Lecture notes

  • Final projects
  • Lecture 29 (Apr 7): solve differential equation using neural networks
  • Lecture 28 (Apr 4): neural networks
  • Python Lab day (Apr 2)
  • Lecture 27 (Mar 31): finite difference method for heat equation
  • Lecture 26 (Mar 28): finite difference method for second-order ODE
  • Lecture 25 (Mar 24): implementing Euler's method on Python
  • Lecture 24 (Mar 21): Euler's method
  • Lecture 23 (Mar 19): separation of variables method and integrating factor method; Worksheet
  • Lecture 22 (Mar 17): differential equations
  • Lecture 21 (Mar 14): Lagrange multiplier (cont.)
  • Lecture 20 (Mar 12): multivariable optimization under constraint, Lagrange multiplier; Worksheet
  • Lecture 19 (Mar 10): practice with multivariable optimization; Worksheet
  • Lecture 18 (Mar 7): multivariable optimization - analytical approach
  • Python Lab day (Mar 5)
  • Lecture 17 (Mar 3): multivariable optimization using Gradient Descent/Ascent method; Worksheet
  • Lecture 16 (Feb 28): directional derivatives, direction of steepest descent
  • Lecture 15 (Feb 26): gradient vectors, dot product; Worksheet
  • Lecture 14 (Feb 24): partial derivatives; Worksheet
  • Lecture 13 (Feb 21): multivariable functions, level sets
  • Python Lab day (Feb 19)
  • Lecture 12 (Feb 14): Newton-Raphson method for root-finding
  • Lecture 11 (Feb 12): bisection method for root-finding
  • Lecture 10 (Feb 10): gradient descent method for single-variable functions
  • Python Lab day (Feb 7)
  • Lecture 9 (Feb 5): PageRank algorithm
  • Lecture 8 (Feb 3): stationary vector of a matrix; Perron-Frobenius theorem
  • Lecture 7 (Jan 31): matrix addition, scaling, and multiplication; Worksheet
  • Python Lab day (Jan 29)
  • Lecture 6 (Jan 27): simplex method for linear programming; Worksheet
  • Lecture 5 (Jan 24): standard form and slack variables
  • Lecture 4 (Jan 22): linear programming problems, graphical method; Worksheet
  • Lecture 3 (Jan 17): solve a linear system of equations using RREF
  • Lecture 2 (Jan 15): reduced row echelon form (RREF); Worksheet
  • Lecture 1 (Jan 13): linear system of equations, elementary row operations

    • Supplement materials

  • Original paper by Sergey Brin and Larry Page on PageRank (1998)
  • Worked-out example of PageRank algorithm

    • Links

    Joseph F. Smith Library, Math Lab
    This page was last modified on Wednesday, Apr 9, 2025.