Department of Computer Science
The University of Texas at Austin

CS 323E - Elements of Scientific Computing (Fall 2026)
Section: 55095, MW 3:30 PM - 5:00 PM, RLP 0.102

Instructor: Dr. Shyamal Mitra
Office Hours: TTH 6:00 pm - 7:00 pm
Zoom Link: On Canvas
E-mail: mitra@cs.utexas.edu
Do not send mail through Canvas.

Required Text: Numerical Methods: An Inquiry-Based Approach with Python
Author: Eric Sullivan
ISBN: 9798687369954

Prerequisites for CS 323E

This is an upper-division Elements of Computing course. You should have taken both CS 303E and CS 313E, or approved substitutions. You should also know mathematics at the level of M408C, M408K, M408N, M408Q, or M408R.

Lectures and Office Hours

This course will meet in one of two modalities: in person or online. We will give at least one week's notice before switching between the two.

When we meet online, lectures and office hours will be held on Zoom through Canvas. When we meet in person, we will meet in the classroom listed above.

For online meetings, make sure you have the latest version of Zoom and log in using your ut_eid@eid.utexas.edu. Lectures will be recorded; these recordings are confidential, intended for educational purposes only, and must not be shared in any form. Any dissemination of the recordings violates University policy and is subject to Student Misconduct proceedings through the Office of Student Conduct and Academic Integrity. Office hours will not be recorded.

Scope of the Course

This is an upper-division Elements of Computing course. You should know basic Python syntax and concepts in data structures and algorithms. The course emphasizes solving scientific equations using numerical methods.

You should understand single-variable calculus, differential equations, and linear algebra; I will provide notes on this background material. We will focus on numerical methods for solving equations rather than on rigorous mathematical derivations. You will complete online courses to fill any gaps in your mathematical background, and we will use standard Python library functions for our solutions.

We will follow the required textbook closely and will provide notes in class for topics not covered in the book; these notes will be posted online. Unlike a traditional lecture format, our classes will be inquiry-based: you will be given problems drawn from real scientific fields, discuss them with your peers, and devise and implement algorithms to solve them.

Learning Objectives

In this course, you will learn how to solve scientific problems numerically. Given a scientific problem, you should be able to This is a programming-intensive course.

Class Participation

You are expected to attend class and participate in class activities such as coding and solving numerical problems. There is a grade associated with being engaged. I will drop your two lowest class participation scores; this number is non-negotiable and should address any reasons you may have for missing class. We will also offer two makeup activities; the maximum score you can earn on a makeup is 80%.

Online Courses

This course requires some background in mathematics. You will be assigned online courses through Coursera and LinkedIn Learning to fill any gaps in your mathematics background. These courses are free, and we think you'll enjoy them. Think of them as reading assignments: when you finish a course, upload a screenshot showing completion to Canvas. Grading is on a binary scale: full credit for completing a course, zero if you do not.

Assignments

There will be weekly programming assignments, drawn from the book or given to you in class. Assignments are due on Mondays. We allow a two-day late period, during which we will accept your assignment with a late penalty of 10 points per day. We encourage you to work on assignments with a partner, but the work you submit must be your own.

Mini-Projects

You will complete four mini-projects this semester. The science will drive these projects: you will treat each one as a research problem to analyze and solve, then present your solution as a scientific paper.

Here are the four areas we will focus on for the mini-projects:

  1. Algebra and Calculus
  2. Linear Algebra
  3. Ordinary Differential Equations
  4. Partial Differential Equations

For these projects, you must work in a group of two. The project reports will be due on Wednesdays: 23 Sep, 14 Oct, 4 Nov, and 2 Dec. There will be a late period of two days, with a 10-point late penalty per day.

Due Dates and Times

All deadlines are expressed in US Central Time. Do not wait until the last hour to submit your work. Systems are sometimes taken down for maintenance and may be unavailable, so plan your schedule accordingly.

Late Add Policy

We welcome late additions to the class. Please take the initiative to reach out to us, connect with your classmates, and form friendships. Join the Discord discussion forum to learn what topics have been covered in class. Feel free to visit us during office hours if you need assistance.

All homework assignments are due after the add/drop period, giving you ample time to complete your first assignment. We will also provide extensions for the online courses. While you may miss a class participation activity, our drop policy will accommodate those absences; however, please note that there are no extensions for class participation.

If you encounter any issues with the course material, please let us know. Visit the instructor's office hours for content-related or administrative questions. Visit the teaching assistants' office hours for questions related to homework assignments, class participation, and projects.

Stance on Generative AI

If your laptop could think, we would call it a thinker. It cannot think; it can compute. That is why we call it a computer. The computer is an excellent tool for problem-solving, but it is we, not the computer, who ultimately solve the problem. We create the algorithms; the computer executes them. The computer handles the tedious, repetitive work, freeing us to focus on the thinking.

Generative AI is a tool that is here to stay. It is designed to assist our thinking, not replace it. The danger lies in becoming overly reliant on it and believing it can think for us. Use this tool with caution, without sacrificing your good thinking habits. AI can be a powerful ally, but it is not a substitute for critical thinking skills.

Our stance on Generative AI use is neutral. Whether you choose to incorporate it into your work is your decision, and we respect it either way; there are no penalties or restrictions associated with its use. That said, we strongly encourage responsible use of Generative AI and the preservation of your critical thinking skills. We are also interested in hearing about your experiences with Generative AI in your work.

Grades

Your performance in this class will be evaluated using your scores for class participation, quizzes, assignments, online courses, and projects. The weight of each component is listed below. There is no extra credit available to improve your grade. We do not drop any scores when computing the weighted average. As an incentive to visit the TAs during their office hours, we will award 5 bonus points toward your total class participation score if you visit the TAs at least 10 times during the semester.

All scores will be entered on Canvas. Check your scores regularly to make sure we have entered them correctly. Note that the average score shown on Canvas is not correct, since it does not apply the weights shown above. Your final grade will be assigned after we compute the weighted average using those weights. Grades will be assigned on the traditional scheme:

We assign grades using the +/- system; the finer cutoffs will be determined at the end of the semester, once the class's weighted average and standard deviation are computed.

Study Groups

To find a compatible person to work with in class, you will pair with a partner for homework assignments and projects. This does not preclude you from working with others in the class.

Communication

We will use Ed Discussion, integrated into Canvas, to discuss class-related questions. Please do not post solutions or code for homework problems on Ed Discussion. All communication with the Teaching Assistants should go through Ed Discussion: if you want to contact a TA, post a private note there rather than sending a private email. If you want to reach me, email me at mitra@cs.utexas.edu. For assignment-related questions, visit the TAs during their office hours; for content-related questions, see me during mine.

We also have an unmonitored discussion group on Discord. You may use this group to find partners for assignments and to discuss other class-related material that does not require a response from the teaching team. We do not encourage forming any other discussion or social group for this class.

Your Responsibilities in This Class

University Time Table

General Policies

If you must be absent from class for the observance of a religious holy day, you may turn in your assignment or paper on an alternate date, provided you give written notice fourteen days before the absence. For religious holy days that fall within the first two weeks of class, notice must be given on the first class day.

Students with disabilities who need special accommodations should contact the Services for Students with Disabilities (SSD) Office (471-6259 or 471-4641 TTY).