Course Syllabus

Instructor Information

Michael G. Kay Instructor

Emailkay@ncsu.edu

OfficeFWH 4161

Phone919-515-2008

HoursTue and Thu, 12–1 pm

Zoomgo.ncsu.edu/kay-office-hours

Jake Benhart Teaching Assistant

Emailjebenhar@ncsu.edu

OfficeFWH 4135

HoursMon and Wed, 2–3 pm

The teaching assistant runs the in-class assessments and returns feedback on submitted work.

Online office hours are by appointment, over Zoom at https://go.ncsu.edu/kay-office-hours. That link is permanent, so no invitation is needed; email me to arrange a time. Students in section 601 should use this route, and a Zoom link for the teaching assistant’s hours is posted in Moodle, so section 601 has the same two hours rather than a separate arrangement.

Preferred Method of Communication and Response Time

Preferred method of communication: email. Expect a response within two business days, not over the weekend. If I email you directly, please respond within two business days.

Which channel is for what. Moodle carries your grades and nothing else. Announcements come by email, through the class list ise-754-fall-2026-everyone@wolfware.ncsu.edu, whose membership comes from the roster, so you are on it from the day you enroll; it is also where you will hear from me if the website or GitHub is unavailable, so do not filter it out. The course website carries the lectures and the schedule, and feedback on your submitted work is written into your repository.

There is no discussion forum. The review activity already records where you had difficulty, and class opens on whatever the class as a whole found hard, so a question raised there reaches both the preparation and the discussion. A forum post would reach neither.

Course Information

Course website: the lectures are published openly at https://mgkay.github.io/ise754f26/. That address names this offering, so the material you are taught stays there permanently and is not overwritten by a later semester. The course page at https://mgkay.github.io/ise754/ carries the schedule and always points at the current offering.

Course materials you clone: https://github.com/mgkay/ise754f26-materials. No account is needed. See Technology Requirements.

Moodle is used for posting grades. Announcements come by email, through the class list.

Meeting time and location: Tuesday and Thursday, 10:15 to 11:30 am, 313 Monteith Engineering Research Center. Section 001 meets in person. Section 601 (distance) does not meet synchronously; recordings are viewed after each class.

Class recordings are reached through the Engineering Online Portal or the university’s Panopto site, in the same way as for any other course. Links to individual recordings are not posted here. This is separate from the short overview videos for particular lectures, which are linked from the course website itself.

Course credit hours: 3

Catalog Description

Elements of logistics networks. Supply chain design: facility location and allocation; great-circle distances; geocoding. Multi-echelon production and inventory systems; sourcing decision systems. Vehicle routing: exact, approximation, and heuristic procedures; traveling salesman problem; basic vehicle routing problem and extensions; backhauling; mixed-mode transportation system design.

Structure

This course is organized so that concepts are delivered before class and class time is used for active work and assessment. For each lecture, three things happen before the class meeting, in this order:

  1. Watch the recorded overview, about twenty minutes, which introduces the lecture’s concepts. It comes first on purpose: it gives you the shape of the lecture before you read it, which is the point at which an overview is worth most.
  2. Read the lecture on the course website. Lectures are written to be read, not presented, and every number in them is computed by code you can run.
  3. Run the review activity in Claude Code, which rehearses the lecture’s worked examples and records what you did.

None of this applies to the first meeting. Lecture 1.1 is the only one you do not read in advance, for the plain reason that there is no in advance: it is the first class. The course overview is given in the room, the toolchain is installed there, and the reading, the recorded overview and the review all begin with lecture 1.2. So the routine above starts Thursday August 20, and the only thing you owe out of the first meeting is the installation report.

Two of the three steps depend on material that is written through the semester rather than ahead of it. Recorded overviews and review activities exist for some lectures and not yet others, and the course website shows which; where one does not exist yet, the reading stands on its own and nothing is owed.

The review activity is not graded. It records completion, so that the class meeting can open with whatever the class as a whole found difficult, but it carries no grade weight of its own. It is preparation, not assessment.

What you do, and what is done for you

The mechanics of all this are the assistant’s job, not yours.

You are not expected to run the mechanics. You are expected to know what happened, and to check it. The assistant pulls, writes files, and puts things where the course looks for them. Three things stay yours: deciding what the model is, deciding whether a result is right, and choosing to submit.

Cadence Happens on its own You ask for, in your own words Yours alone
Once, at setup the guide detects what is already installed and skips it “work through SETUP.md” paste the report into Moodle
Every session the session check reports feedback you have not pulled, and work you have not pushed nothing notice it
Before each class, from 1.2 on the assistant pulls what is new, says what arrived, and copies a lecture’s script into work/lectures/ when you want to run it “get me ready for Thursday” watch, read, and answer the review honestly
Before each homework handouts/ pulled; work/hw<N>/ created; files land where the tooling looks “start homework 2” the modeling, and the checking
Before each project releases pulled into work/project-<N>/ “what should I ask the client?” deciding what to ask
To submit nothing, deliberately “submit this” the decision to submit

How the pieces fit together

Five places, and each does one thing. Everything you read is public and needs no account; everything you write is private to you and the teaching staff.

How the course fits together: what is public on GitHub, the three folders on your computer, and what is behind an NC State sign in.

Read in a browser: the course website, the overview videos, and Moodle. Nothing there needs setting up.

Kept in step with git: the three folders. materials/ and handouts/ are things you receive, so you only ever git pull them. work/ is the one you write in, and you git push it to your own repository.

Feedback comes back the same way. It is written into your repository as commits rather than emailed, so it reaches your computer only when you git pull, and you learn it is there only if you have turned on that repository’s notifications. Both steps are in SUBMITTING.md, and both are worth doing the first time you clone, because feedback you never pull is feedback you never read.

The two halves are different places. Everything public needs no account of your own: the website and the materials repository are on my GitHub account, and the overview videos are on YouTube. Everything private, meaning the handouts, your own repository, and Moodle, is on NC State’s systems and needs you signed in. Announcements arrive by email, and Moodle notifies you when a grade is posted; it carries nothing else.

Setting up your computer

The first class is spent installing the toolchain, and the written setup guide takes you through it step by step: see SETUP.md at https://github.com/mgkay/ise754f26-materials. The last step runs bootstrap_check.jl, which checks the whole installation mechanically and prints either READY or the specific reason it is not.

Paste that output into Moodle before the second class meeting, Thursday August 20. Like the review activity, this is required but not graded: it records that your machine works, and the list of what broke across the class shapes what that meeting covers. Running the check is also the first instance of something the course returns to constantly, which is verifying a result mechanically rather than trusting that it looks right.

If your machine will not cooperate, a locked-down work laptop, a blocked execution policy, or no administrator rights, say so by that second meeting rather than working around it silently. There is no penalty. Completion is recorded when the problem is resolved, and the usual routes are a personal machine or a campus lab; if neither works we will sort out something individually. What does not work is staying quiet, because every lecture from the third onward assumes a working toolchain.

Class meetings then open with the points the class as a whole found difficult, include a short written assessment done on paper, and spend the remainder on active work: worked problems, building the exam study guide together, and practice with the computational tools.

Every meeting includes a short written assessment, about twenty minutes, on paper. On the meetings when a homework or project is due, it assesses that work and it carries grade weight. On the other meetings it is a brief review of the previous lecture and carries little or none. That second kind is also when a missed assessment is made up, so nothing has to be scheduled outside class.

The course consists of six topics across 24 lectures: Introduction, Location, Transport, Networks, Routing, and Inventory. There is a homework assignment roughly every second or third lecture and a project for each topic block, six in all, with none for the Introduction. There are three examinations: two during the semester and one during the final examination period. All three are taken in class, on paper, without a computer.

Prerequisites and Corequisites

Basic knowledge of operations research, similar to OR/ISE 501.

Minimum Technical and Digital Information Literacy Skills

Required technical skills

  • Navigate and use Moodle, NC State’s learning management system.
  • Install software on your own computer: Julia, Visual Studio Code, and Claude Code. Full instructions are given in the first lecture.
  • Use a command line at a basic level.
  • Use git and GitHub at a basic level: clone a repository, commit a change, and push it. No prior experience with git is assumed. Everything needed is taught in the course, and a written guide is provided.
  • Read and modify a short Julia script. Writing Julia from scratch is not required; reading it is.
  • Download and upload attachments.

Required digital information literacy skills

  • Perform online research using search engines and library databases.
  • Use online search tools for academic purposes, including search criteria, keywords, and filters.
  • Analyze digital information for credibility, currency, and bias.
  • Properly cite information sources, including AI-generated content.

General Education Program (GEP) Information

GEP category fulfilled: none. GEP corequisites: none.

Learning Outcomes

Course objectives. This course provides a deep understanding of logistics systems by introducing the models and analytical techniques to evaluate their design and operation. Emphasis will be placed on the development of models to demonstrate the core concepts involved in network design, distribution strategies, vehicle routing and scheduling, and inventory management.

Upon successful completion of this course, students will have an increased capacity to:

  • LO1: demonstrate an essential understanding of logistics systems
  • LO2: identify various cost components involved in real-world problems
  • LO3: formulate problem components as mathematical optimization models
  • LO4: develop solution techniques to tackle complexities involved in the problems
  • LO5: conduct numerical experiments and analyze the trade-offs
  • LO6: analyze the sensitivity of costs to various components/input data

This quantitative, or engineering, approach to logistics is meant to complement that of business logistics, which is concerned with the management of physical distribution and procurement processes and relationships, and supply chain management, which also includes the coordination of product design, marketing, sales, and finance, both within and between firms, to best support the operation of a firm’s supply chain.

How this offering pursues those outcomes

The six outcomes above are the course’s and do not change between offerings. How this offering reaches them does, and the change is smaller than the presence of AI might suggest.

What has not changed. This course has always been taught with a scripting language, Julia now and MATLAB before it, and students have never been expected to write programs. You are given vetted components and you compose them into a script that produces a result. Work has always been submitted as a script that runs, rather than as a bare answer, so that it can be inspected and rerun with different numbers. And the in-class final examination has always tested by-hand understanding of the principal algorithmic constructs. All three continue exactly as before.

What has changed is the level at which you can compose. An AI coding assistant makes it practical to reach problems that would previously have taken far too long to set up, so the scope of what you can attempt expands considerably. It also makes producing an answer cheap, and a cheap answer is not the same as a correct one: these tools produce wrong results as fluently as right ones. So the scarce skill shifts from generating a result to judging whether one can be trusted, and this offering puts more weight there than earlier ones did.

That is a shift of emphasis, not a new subject, and it sits inside the six outcomes rather than beside them. LO3 is the modeling convention used throughout the lectures, where a model is stated as a word model, then a formulation, then pseudocode, then runnable code. LO5 and LO6 are where verification lives: running a numerical experiment to check a result by an independent route, and pushing an input to see whether the answer moves the way it should, are the two techniques drilled hardest. LO4 is exercised by the projects, which are large enough that the solution technique matters.

One habit to expect that may be unfamiliar. You will routinely be asked to state what you expect before running anything. An estimate written after seeing the answer is not a check, and the order is the whole point.

Course Materials

Required textbook: none. All lecture material is published on the course website.

Other required materials: none.

Technology Requirements

The following are used in this course. All are installed in the first week, with instructions given in the first lecture.

Tool Purpose Cost
Julia The computational language used throughout Free
Visual Studio Code The editor used to work with Julia and Claude Code Free
Claude Code The AI coding assistant used for homework and projects Paid, see below
GitHub (NC State Enterprise) Where you submit your work Free, through NC State
Moodle Grades Free
Panopto Recordings of the class meetings, reached the way recordings are reached in every course, not through links posted here Free
YouTube Short overview videos for individual lectures, linked from the course website Free

Minimum machine. Any reasonably current laptop will do, but the specific floors are set by the tools rather than chosen arbitrarily:

Minimum Why
Operating system macOS 13 or later, or Windows 10 version 1809 or later, 64-bit. Linux is not supported Claude Code’s stated requirement, and the highest of the three. The setup instructions are written and tested for macOS and Windows only
Memory 8 GB, 16 GB comfortable Claude Code alone requires 4 GB; you will run it alongside Visual Studio Code and Julia, and building the course’s plotting packages is memory-hungry
Free disk space 15 GB Julia’s package cache dominates this. On the machine the lectures are built on it measures about 16 GB, most of it precompiled code and native graphics libraries rather than the packages themselves
Network A reliable connection Claude Code requires internet access to function at all

Every tool the course uses runs on Linux, but the setup instructions are written and tested for macOS and Windows only. If a Linux machine is your only one, contact me before the course starts.

You install one thing yourself. Install Claude Code. Everything else, Visual Studio Code, Julia, and the extensions, is installed for you: Claude Code downloads a setup file supplied by the course and works through it, checking what you already have and installing only what is missing. The written instructions are at https://github.com/mgkay/ise754f26-materials and are available before the first class. Julia is fixed at version 1.12.6 for the whole semester, so that everyone’s environment behaves the same way and the numbers you compute match the lectures; do not update it mid-semester.

Turn on repository notifications the first time you clone your own repository. GitHub does not notify you by default when teaching staff write into it, and feedback arrives that way. SUBMITTING.md gives the steps.

Other Student Expenses

A Claude Pro subscription, currently $20 per month, is required, purchased by you at https://claude.com/pricing. Choose monthly billing, not annual: the annual option saves three dollars a month and commits you to twelve, and monthly means a student who drops stops paying.

An existing Pro or Max plan is fine, as is an employer’s Team or Enterprise plan if it includes Claude Code; a free account needs upgrading. There are no course fees and no textbook.

If the cost is a genuine difficulty, speak to me before the course drop deadline. I would rather know early than have someone struggle quietly, and the university’s own hardship resources are listed under Student Resources below.

Communication Guidelines

Respecting our learning community

The NC State Code of Student Conduct outlines expectations for behavior in the classroom, whether virtual or physical, and the consequences for students who violate them. Any behavior that impacts other students’ ability to learn and succeed will be addressed, but expressing diverse viewpoints and interpretations of course content is welcome.

Community guidelines for this course:

  • Use a respectful tone in all forms of communication.
  • Maintain professionalism in written communication.
  • Respect regional dialects and culturally embedded ways of oral communication.
  • Stay home if you are exhibiting symptoms of a contagious illness.
  • Refrain from lewd or indecent speech or behavior, help maintain a safe physical environment, do not use a phone for voice or text communication except when explicitly given leave, and do not attend class under the influence of any substance.
  • Do not record others without their consent, and do not engage in any form of hazing, harassment, intimidation, or abuse.
  • Respect cultural differences that may influence communication styles and needs.

Plan for interaction between instructors and students

Outside the classroom, instructors and students interact primarily through email, office hours, and written feedback returned on submitted work through GitHub.

Expectations for learner participation and interaction

Class meetings involve working problems together and building the examination study guide as a class. Section 601 students participate asynchronously through the recorded sessions and the submitted work.

Grading and Feedback

Grading criteria and details

Most of the grade for homework and projects is earned in class, on paper, without a computer. Take-home work is worth completion credit; what it prepares you for is the in-class assessment of the same work, and that is where the grade lives. This is deliberate, and the reason is stated plainly: an AI tool can produce a take-home solution, so a take-home solution cannot certify what you can do. An assessment written by hand, in the room, can.

Component Weight Take-home, AI-permitted In class, AI-free, on paper
Examinations 70% — 70%
Projects 18% 12% on the submitted work 6% authentication
Homework 12% 4% completion 8% assessment
Total 100% 16% 84%

Eighty-four percent of the grade is earned in class, on paper, with no computer and no AI tool.

  • Examinations, 70%. Three: Exam 1 (21%) Thursday October 1, Exam 2 (21%) Thursday November 5, and the final (28%) Tuesday December 8, 8:30 to 11:00 am. All are individual, in class, on paper, closed computer, non-programmable calculator. Grades are returned within two weeks of each of the two in-class examinations. The final examination is not returned during the semester, because it falls after the last class meeting and grades are due to the university shortly afterward. Final grades appear in MyPack on the university’s schedule. I keep the final examination papers, and you are welcome to look at yours in my office the following semester.

  • Homework, 12%. Six assignments, one per topic, with Location taking two. Submitting earns 4% in aggregate; the remaining 8% is earned in class, in a short written assessment on the day the homework is due. A homework is always due at least five days after it is assigned.

  • Projects, 18%. Five projects. Unlike homework, the work you submit is graded on its merits and carries 12%. The remaining 6% is a short in-class assessment at the following meeting, whose purpose is to confirm that you can account for the work you handed in. A project is always assigned at least one class meeting before it is due, and that intervening meeting is where you can raise questions about the approach you are taking.

    Projects are not on the examinations, and they are still where the most interesting work happens. Because every student’s project is different, no examination question could be drawn from them fairly. That also means projects are free of the constraints an examination imposes: they can use computation at full scale, real and imperfect data, and problems too large to work by hand. They are where the course does what a written examination cannot reach, which is why the submitted work is graded rather than merely checked off.

Every graded in-class assessment is a twenty-minute block, and each is worth a little over one percent of the course. Work is due at 8:00 pm the evening before the meeting at which it is assessed.

Students in section 601 (distance) have different weights, because the in-class assessments cannot be administered remotely. See Distance Education (Section 601) below.

Modifications to the timing of grades or feedback, if required, will be announced by email.

Grading scale and performance calibration

This course uses standard NC State letter grading:

Low Letter High
97 ≤ A+ ≤ 100
93 ≤ A < 97
90 ≤ A- < 93
87 ≤ B+ < 90
83 ≤ B < 87
80 ≤ B- < 83
77 ≤ C+ < 80
73 ≤ C < 77
70 ≤ C- < 73
67 ≤ D+ < 70
63 ≤ D < 67
60 ≤ D- < 63
0 ≤ F < 60

The scale above represents the minimum guaranteed letter grade for a specific numeric score. However, the numeric thresholds required for final letter grades may be lowered, at my determination, to align with the actual difficulty of the assessments, particularly on examinations. Thresholds are set individually, so an adjustment may apply to some letter grades and not to others, and it applies most often at the upper end of the scale, where the last points are the hardest to earn. These adjustments preserve the strict ordering of grades: a student with a higher total numeric score will always receive a final letter grade equal to or higher than a student with a lower numeric score.

Requirements for earning a grade of “Satisfactory”

If you are taking this course for credit only (S/U), your grade will be reported as S (Satisfactory) when coursework is equivalent to a C- or better, or U (Unsatisfactory) when it is less than a C-. For more information, see REG 02.20.15, Credit-Only Courses.

Requirements and procedures for auditing this course

Auditing is approved on a case-by-case basis. Contact the instructor for approval. Refer to REG 02.20.04, Audits for more information and required forms.

Examinations

All three examinations are taken in class, on paper, individually, with a closed computer and a non-programmable calculator. No AI tool is available during an examination.

  • Notes. One double-sided sheet of notes for each of the two semester examinations. Two double-sided sheets for the final.
  • A formula sheet is printed as part of each examination. You do not need to spend note-sheet space on formulas that will be supplied. Which formulas are on it is published as part of each examination’s study guide, and the sheet grows lecture by lecture, so you always know what will be provided.
  • A study guide is published for each examination, built from homework and review problems you have already seen and assembled with the class during the semester. Examination questions are variants of study-guide problems.
  • Examinations assess both halves of the course. Some questions ask you to set up or reason about a model. Others give you a computational result, sometimes two competing results, sometimes with an error in it, and ask you to check it and give a justified verdict.
  • Where examination questions come from. The lectures supply the worked examples, the review activity rehearses them, homework applies the method to new problems, the study guide collects what is worth revisiting, and the examination asks a variant of it. Nothing appears on an examination that has not travelled that path, which is why the study guide is a reliable guide to what will be asked.
  • Projects are not covered on the examinations. Each student’s project is different, so no examination question could be drawn from them and be fair to everyone. Project work is assessed entirely by its own in-class assessment.
Date Coverage
Exam 1 Thursday, October 1 Lectures 1.1 to 2.7
Exam 2 Thursday, November 5 Lectures 3.1 to 4.4
Final Tuesday, December 8, 8:30 to 11:00 am, 313 Monteith Lectures 5.1 to 6.3, with some cumulative material

The final examination starts at 8:30 am, not at the usual 10:15.

Distance Education (Section 601)

This section applies only to students enrolled in section 601. Everything else in this syllabus applies to both sections.

How the course reaches you. Almost all of it already works at a distance, because the course is built so that concepts are delivered before class rather than during it. The written lectures are on the public course website, the overview videos are public where they exist, and the pre-class review activity runs on your own machine on your own schedule. Class meetings are recorded and reached through the Engineering Online Portal, and are normally viewed after the meeting rather than live. You are not expected to attend synchronously.

Setting up your computer. Section 001 installs the toolchain together in the first class meeting. You do that part on your own instead, working through SETUP.md, which is written to be followed without help and has been tested on both macOS and Windows. That is not a disadvantage in timing: the guide is available before the first meeting, so you can start whenever suits you rather than waiting for it. The recording of that meeting appears afterwards and shows the same procedure if you would rather watch it done first. The check is due before the second meeting, as it is for section 001, and if something will not work, email me rather than going quiet about it.

Examinations. All three examinations are taken under a proctor, at a time arranged in advance. Proctoring is arranged through Engineering Online, not with me. Follow their process and their deadlines; I supply the examinations to them directly.

Grading. Only the in-class portions cannot be administered remotely, so only those transfer to the examinations. Your project work is graded on its merits exactly as it is for section 001, since that grading is of the work you submit and needs no classroom:

Percentage Component Details
84% Examinations Exam 1 (25%), Exam 2 (25%), Final (34%), taken under a proctor
12% Projects Graded on the submitted work, the same as section 001
4% Homework Completion credit for the submitted work

The grade boundaries, meaning the score-to-letter conversion in the Grading scale table above, are the same for both sections. Only the weights differ: the 14% that section 001 earns in class is what moves to the examinations.

Deadlines are the same as section 001. Homework and project work is due at 8:00 pm Eastern on the evening before the class meeting at which section 001 is assessed on it, whether or not you view that meeting. The dates are on the schedule page. The time is absolute, so nothing about your deadline depends on attending anything.

Attendance is not graded or recorded for either section, and section 601 students are not expected to attend.

Self-checks, so you are not flying blind. You receive the same items section 001 writes in class, as ungraded self-checks, with solutions released after the corresponding class meeting. They are not graded and they do not affect your score directly, but submitting them is part of what your completion credit requires, and they are the best signal available of how you are doing before an examination arrives.

Office hours. In addition to the scheduled in-person office hours, I am available by videoconference or telephone by appointment. There is no standing remote slot; email me and we will arrange a time, over Zoom at https://go.ncsu.edu/kay-office-hours.

Submitting work is identical for both sections and is described below; nothing about it requires being on campus.

Submitting Work

Work is submitted through git, to a private repository created for you inside NC State’s GitHub Enterprise organization ncstate-engr-ise and named ise754-f26-<unityid>. Only you, the instructor, the teaching assistant, and NC State IT administrators can read it. No other student can see your work.

Submitted work is read to prepare class, and code you submit may be run to check that it produces what it claims. Anything automated only raises things for a person to look at; it never assigns a grade or clears a submission, and a person decides in every case.

Three practical notes.

  • You must be added to the organization by NC State IT before you can submit. If you add the course late, submission is unavailable until IT has acted, typically a day or two. Enrollment changes cluster until census day, about ten days into the semester, so expect some friction in the first weeks. Contact the instructor as soon as you enroll.
  • The first time you clone a repository, GitHub will email you saying that a first-party GitHub application, Git Credential Manager, has been authorized to access your account. This is expected. It is part of signing in and is not a security incident.
  • Nothing is submitted before the meeting on Tuesday, August 25, so there is class time to work through the setup. That first submission is due at 8:00 pm on Monday, August 24, the evening before.

Full setup instructions are provided as a separate written guide, SETUP.md, at https://github.com/mgkay/ise754f26-materials.

Course Schedule

The semester schedule, with lecture dates, assignment dates, and due dates, is published on the course website and is the authoritative version. Please note that it is subject to change.

Class meets Tuesday and Thursday, 10:15 to 11:30 am, from August 18 through December 1. There is no class on Tuesday September 29 (Wellness Day), Monday October 19 and Tuesday October 20 (Fall Break), or Thursday November 26 (Thanksgiving).

The two examinations are Thursday October 1 and Thursday November 5, both in class. The final examination is Tuesday December 8, 8:30 to 11:00, in the regular room.

Every meeting’s assigned work, due dates, and in-class assessment are on that page. Two rules govern the dates: a homework is due no fewer than five days after it is assigned, and a project is assigned at least one class meeting, and never fewer than seven days, before it is due.

Course Policies

Attendance

Attendance is not graded and is not recorded. There is no roll call and no sign-in. No points are awarded for being present.

But twenty percent of the grade for section 001 is earned during class meetings, in short written assessments that cannot be completed elsewhere, so students are expected to attend. A student who is not there does not do that assessment and owes it.

If you miss an assessment, you can make it up, and you do not need an excuse. Most meetings carry a low-weight review assessment rather than a graded one, and in that slot you are handed the item you missed while the rest of the class works on the current one. One make-up per meeting, so a backlog takes as many meetings to clear as it took to build; anything still outstanding at the last meeting scores zero. If you have missed several and want to clear them faster, come to office hours.

A make-up examination or final examination will be scheduled for a student with an excused absence.

Students in section 601 are not expected to attend, and nothing in their grade depends on attendance. See Distance Education (Section 601).

Related NC State policy: REG 02.20.03, Attendance Regulations.

Late assignments

Take-home homework and project deliverables are due at 8:00 pm Eastern on the evening before the class meeting at which they are assessed, because submitting the work is what makes you eligible for the assessment. Submissions are collected at that time and reviewed together, which is how the next morning’s class is prepared. This deadline is the same for both sections, and it is an absolute time rather than one tied to attending a meeting.

Work submitted late loses its completion credit, and if it is not submitted before the assessment begins, the assessment cannot be taken at that meeting and becomes a make-up.

Incomplete grades and withdrawals

Information on incomplete grades is in REG 02.50.03, Grades and Grade Point Average. If you encounter a serious disruption not caused by you and would otherwise have completed the course successfully, contact the instructor as soon as you can to discuss an incomplete, including an agreement on when the remaining work must be done.

Withdrawal from a course or from the university is governed by REG 02.05.04, Term Withdrawal from the University. This is a graduate course, so the applicable procedure is the one in Graduate Handbook §3.16, Withdrawal from the University: withdraw through MyPack Portal (Planning & Enrollment → Term Withdrawal/Leave of Absence), after consulting your advisor and your Director of Graduate Programs. Students in section 601 should also see Engineering Online’s Withdrawal Procedures.

Use of AI Tools

This course requires the use of an AI coding assistant, and the policy has two halves.

On take-home work, AI use is expected and unrestricted. Claude Code is a required tool for homework and projects. Use it freely to write code, explore approaches, and check your reasoning. There is nothing to disclose and nothing to apologize for; using it well is part of what the course teaches.

On examinations and on in-class assessments, no AI is permitted. These are done on paper with a non-programmable calculator, and they are where your grade is determined. This is the whole design in one sentence: the tool is unrestricted where it helps you learn, and unavailable where the course certifies what you can do.

You are responsible for what you submit. An AI tool produces fluent, confident, well-formatted output whether or not it is correct, and you take full responsibility for any error in work you hand in. The course teaches a specific discipline for checking such output, and homework requires you to show the checks you ran and the verdict you reached, not just the answer.

Your work is read by automated tools. Submitted work is read with AI assistance, both to give feedback and to identify where the class as a whole is struggling. Two commitments about that. The automated pass only raises things for a person to look at; it never assigns a grade and never clears work. Every grade in this course is assigned by a person.

Content directed at an automated reviewer is an integrity violation. Text placed in a submission in order to influence an automated tool, for example instructions addressed to a reviewer or grader, or hidden text intended to be read by software rather than by a person, violates the academic integrity policy. It is treated as a violation whether or not it succeeds and whether or not the work is graded. If you believe an automated review has assessed your work incorrectly, raise it with me; that is what the appeal path is for.

University Policies

Academic integrity and honesty

Students are required to comply with the university policy on academic integrity found in the Code of Student Conduct, REG 11.35.05, sections 8 (Academic Misconduct) and 9 (Sanctions for Academic Misconduct). Students are required to uphold the Pack Pledge: “I have neither given nor received unauthorized aid on this test or assignment.” Violations of academic integrity will be handled in accordance with the Student Discipline Procedures, REG 11.35.02. See the Academic Integrity page maintained by the Office of Student Conduct for a detailed explanation of the university’s policies.

Student privacy

Originality checking software. Software may be used in this course to detect the originality of student submissions. Use of AI tools as described above is permitted and expected; submitting as your own work material copied directly from another student is not permitted.

Class recording statement. In-class sessions are recorded in a way that may also record students in this course. These recordings are made available to students enrolled in the course, including section 601, and will not be used beyond the current semester or in any setting outside the course. Recording is stopped during in-class assessments.

Class privacy statement. This course requires online exchanges among students and the instructor, but not with persons outside the course. Students may be required to disclose personally identifiable information to other students, where relevant to the course. All students are expected to respect the privacy of each other by not sharing or using such information outside the course.

Note on submitted work. Each student’s submitted work is stored in a private repository visible only to that student, the instructor, the teaching assistant, and NC State IT administrators. No student can see another student’s submitted work.

Other policies

Students are responsible for reviewing the NC State University PRRs which pertain to their course rights and responsibilities. These are the policies, regulations and rules that REG 02.20.07 §2.2 requires every syllabus to link:

Electronically hosted course components, listed as REG 08.00.11 requires, with any that carry privacy or accessibility implications identified:

Component Hosting Note
Moodle NC State The course home page, as the regulation requires. Grades, and the first assignment’s submission
MyPack NC State Registration and the externally facing syllabus
Panopto NC State Class recordings. Scoped to this course’s enrolled students, including section 601, and not used beyond the current semester
WolfWare Google Group (ise-754-fall-2026-everyone@wolfware.ncsu.edu) NC State The class list, provided through WolfWare by DELTA with membership drawn from the roster. Covered by the university’s Google Workspace for Education agreement, under which Google is a “school official” under FERPA with respect to education records
Course website (mgkay.github.io/ise754f26) GitHub Pages Public, no account needed, no student data. Lectures and schedule only
Course materials (github.com/mgkay/ise754f26-materials) GitHub Public, no account needed, no student data
Submission repositories NC State GitHub Enterprise Private, one per student. Visible only to that student, the instructor, the teaching assistant, and NC State IT administrators
Claude (Anthropic) Third party You purchase this yourself and hold your own account. Work you put through it goes to a provider outside the university. Do not put another person’s identifiable information into it. See Use of AI Tools
Overview videos YouTube Public, no account needed, viewing not tracked by the course

If any of these presents an accessibility barrier, contact me and the Disability Resources Office early, and an alternative will be arranged. The graded core of the course is written and in class, so no assessment depends on any of these tools.

Student Resources

Academic and Student Affairs maintains a website with links for student support on campus, including academic support, community support, health and wellness, and financial hardship or insecurity. See Find Help on Campus.

Disability resources

Reasonable accommodations will be made for students with verifiable disabilities. To take advantage of available accommodations, students must register with the Disability Resources Office (DRO), which is part of the Office of Equal Opportunity. For more information, see the DRO website and REG 02.20.01, Academic Accommodations for Students with Disabilities.

Students with accommodations should contact the instructor early. The graded core of this course is timed, written, and in class, so accommodations such as extended time or a separate room need to be arranged before the first assessment, which is in the second week.

Mental health and wellness

As a student, you may experience a range of personal issues that can impede learning, such as strained relationships, increased anxiety, alcohol or drug concerns, feeling down, difficulty concentrating, or lack of motivation. The Counseling Center at NC State offers confidential mental health services for full-time NC State students, including same-day emergency services. Individual appointments can be made by visiting the Counseling Center Get Started page and following the instructions under Make an Appointment. A full overview of campus wellness resources is on the Wolfpack Wellness website. Please do not hesitate to get connected early for the support you need to be successful. Additional sources of support include reaching out to your embedded counselor or using a drop-in space. Additional resources include Pack Essentials if you have food insecurities, and the NC State CARES program, where you can refer friends or classmates in need of immediate support.

Getting help

At NC State, we take the health and safety of students, faculty and staff seriously. The Office of Equal Opportunity supports the university community by providing services and resources to support and guide individuals in obtaining the help they need, including in matters of discrimination, harassment and interpersonal violence. See its Get Help page.

Supporting fellow students in distress

As members of the NC State Wolfpack community, we each share a personal responsibility to express concern for one another and to ensure that this classroom and the campus as a whole remain a healthy and safe environment for learning. If a classmate’s personal behavior concerns or worries you, either for their well-being or yours, report it through go.ncsu.edu/CARES or by calling 919.515.4405 between 8 a.m. and 5 p.m. You can report anonymously, but sharing your contact information lets them follow up with you personally.

Course Evaluations

ClassEval is the end-of-semester survey for students to evaluate the instruction of all university classes. It is administered online and includes 12 closed-ended and 3 open-ended questions. Deans, department heads, and instructors may add a limited number of their own questions.

Each semester students’ responses are compiled into a ClassEval report for every instructor and class. Instructors use the evaluations to improve instruction and include them in their promotion and tenure dossiers, while department heads use them in annual reviews. The reports are included in instructors’ personnel files and are considered confidential.

Online class evaluations are available during the last two weeks of the semester. Students receive an email directing them to the website. Evaluations become unavailable at 8 am on the first day of finals. Contact the ClassEval Help Desk at classeval@ncsu.edu.

Syllabus Modification Statement

Our syllabus represents a flexible agreement. It outlines the topics we will cover and the order in which we will cover them. Dates for assignments represent the earliest possible time they would be due. The pace of the class depends on student mastery and interests, so minor changes can occur if we need to slow down or speed up the pace of instruction.