CSC338 Numerical Methods (UTM)

Winter 2020

Exam FAQ

Course Description

Computational methods for solving numerical problems in science, engineering and business. Linear and non-linear equations, approximation, optimization, interpolation,integration and differentiation. The aim is to give students a basic understanding of floating-point arithmetic and the implementation of algorithms used to solve numericalproblems, as well as a familiarity with current numerical computing environments.Course concepts are crucial to a wide range of practical applications such as computational finance and portfolio management, graphics and special effects, data mining and machine learning, as well as robotics, bioinformatics, medical imaging and others.

See the Syllabus for more information.

Course Staff and Contact

Instructor: Lisa Zhang
Office Hours: Monday 2pm-4pm and by appointment
Office: DH3078
Email: lczhang [at] cs [dot] toronto [dot] edu
Teachin Assistant:
Please include "CSC338" in your subject.

All announcements will be made on Piazza and Quercus


Michael Heath, Scientific Computing: An Introductory Survey, Second Edition, McGraw Hill, 2002.

Roughly the first half of the book will be covered. The relevant chapters have been made available by McGraw Hill in the textbook store at a special price.

Tentative Schedule

The course schedule is tentative and subject to change.

Lecture 1 (Jan 8) Tutorial 1 (Jan 9) - Optional
  • Python and Jupyter Notebook
  • Setting up Homework 1
Recommended Review
Lecture 2 (Jan 15)Tutorial 2 (Jan 16)
  • Floating Point Numbers Exercises
  • Handout
ReadingHomework 1 (due Jan 15)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 3 (Jan 22)
  • Systems of Linear Equations
  • Gauss Elimination
  • LU Factorization
  • Lecture Notes
Tutorial 3 (Jan 23)Reading
Additional Reading
Homework 2 (due Jan 22)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 4 (Jan 29)
  • Vector and Matrix Norms
  • Conditioning of Ax=b
  • Gauss Elimination With Partial Pivoting
  • Lecture Notes
Tutorial 4 (Jan 30)ReadingHomework 3 (due Jan 29)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 5 (Feb 5) Tutorial 5 (Feb 6)ReadingHomework 4 (due Feb 5)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 6 (Feb 12)
  • Linear Least Squares
  • The Normal Equation
  • Conditioning & Sensitivity
  • Lecture Notes
Tutorial 6 (Feb 13)
  • Homework Take-up
ReadingHomework 5 (due Feb 12)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Reading Week.Instructor Office Hours Wednesday Feb 17th 12pm-2pm DH3078
Lecture 7 (Feb 26)No tutorials this week
Lecture 8 (Mar 4)Tutorial 8 (Mar 5)ReadingHomework 6 (due Mar 4)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Data Files [mnist_images.npy] [mnist_labels.npy]
Lecture 9 (Mar 11)
  • Fixed-Point Iteration
  • Newton's Method; Secant Method
  • Non-Linear Equations Definitions & Setup
  • Lecture Notes
Tutorial 9 (Mar 12)ReadingHomework 7 (due Mar 11)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 10 (Mar 18)
  • Nonlinear Optimization
  • Golden Section Search
  • Newton's Method
  • Notes
Tutorial 10 (Mar 19)
  • Nonlinear Equations
ReadingHomework 8 (due Mar 18)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 11 (Mar 25)Tutorial 11 (Mar 26)ReadingHomework 9 (due Mar 25April 1)
Handout and Starter Code [html] [pdf] [ipynb] [py]
Lecture 12 (Apr 1)No TutorialReading
  • N/A. Materials not on exam.

Final Examination Schedule

You can find old csc338 exams here. Keep in mind that the content covered each term may be different.