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[COMP585_Fall2025] Intelligent Software Systems

General Information

Instructor Jin Guo
TA Avinash Bhat
Class Time F 9:35 am - 12:25 pm
Location ADAMS 211
Primary Platforms GitLab and Slack (link on MyCourses)

Policies

Attendance

The lectures consist of in-class activities to motivate discussion and collaborative learning. In-person participation is required, and activities during the lecture are graded. Therefore, you should only consider taking this course when you can guarantee attendance throughout the semester. Occasionally, if there are unforeseen reasons that prevent you from attending the lectures in person, we will try to accommodate, but you need to contact us prior to the lecture.

Communication

Since this course is highly collaborative, you need to pay special attention to the communication code. The University is committed to maintaining teaching and learning spaces that are respectful and inclusive for all. To this end, offensive, violent, or harmful language arising in course contexts may be cause for disciplinary action under Article 10 of the Code of Student Conduct and Disciplinary Procedures and Section 2.7 of the Policy on Harassment, Sexual Harassment, and Discrimination Prohibited by Law.

Description

This course explores how to design and build an intelligent system from the perspective of software design and engineering and human-computer interaction, touching topics from requirements and design to deployment and maintenance, as well as ethical considerations throughout the process.

While this course will touch on topics in MLOps, it focuses more on understanding and reflecting on the principles and considerations of designing and engineering aspects of intelligent software. You will gain a basic understanding and hands-on experience of the primary MLOps tools and their implications through the group project.

This course will NOT teach machine learning (ML) techniques. Our school has many courses on that, across different levels and topics. We assume that you have already acquired the essential knowledge in ML or wilwl catch up by yourself throughout the semester.

Prerequisite

COMP 303, COMP 424/COMP 551 (or equivalent background)

Reference Material

We will not concentrate on any particular resources. Instead, the readings will include content from book chapters, research papers, blog posts, talks, etc. The pointers to those content will be added to the schedule prior to the lectures.

Assessment and Evaluation (Tentative)

Assessment Method Weight
Activities 30%
Assignment 30%
Project 40%
  • Activities will be conducted during the lecture. However, some of the preparation is required prior to the lecture. Your grade on the activity component will be the average of all activities, with the least performing or missing one discarded. This means that you can miss one activity (due to sickness or other personal reasons) without the calculation of your overall grade being impacted.

  • Any form of plagiarism and cheating is strictly banned. Integrity is crucial to this course and your future career. Any violation against academic integrity will be taken very seriously. For more information, please refer here.

  • Related to the use of generative AI tools in any forms of written reports (including presentation slides), we follow the ACM Policy on Authorship. This policy also applies to the group project. In particular,

  • If you are using generative AI software tools such as ChatGPT, Jasper, AI-Writer, Lex, or other similar tools to generate new content such as text, images, tables, code, etc. you must disclose their use in either the acknowledgements section of the Work or elsewhere in the Work prominently. The level of disclosure should be commensurate with the proportion of new text or content generated by these tools.
  • If entire sections of a Work, including tables, graphs, images, and other content were generated by one of these tools, you should disclose which sections and which tools and tool versions you used to generate those sections by preparing an Appendix or a Supplementary Material document that describes the use, including but not limited to the specific tools and versions, the text of the prompts provided as input, and any post-generation editing (such as rephrasing the generated text). Authors should also note that the amount or type of generated text allowable may vary depending on the type of the section or paper affected. For example, using such tools to generate portions of a Related Work section is fundamentally different than generating novel results or interpretations.
  • If the amount of text being generated is small (limited to phrases or sentences), then it would be sufficient to add a footnote to the relevant section of the submission utilizing the system(s) and include a general disclaimer in the Acknowledgements section.
  • If you are using generative AI software tools to edit and improve the quality of your existing text in much the same way you would use a typing assistant like Grammarly to improve spelling, grammar, punctuation, clarity, engagement or to use a basic word processing system to correct spelling or grammar, it is not necessary to disclose such usage of these tools in your Work.

Schedule

This schedule is subject to adjustments and will be updated regularly.

Week Class Date Content Reading Note
1 29 Aug Human Intelligence and Intelligent Systems
2 5 Sep ML Model Quality
3 12 Sep From Model to System
4 19 Sep Data Management
5 26 Sep Human Need and Requirements
6 3 Oct Contestability
7 10 Oct Team and Collaboration
8 Study Break
9 24 Oct Project Midterm Presentation
10 31 Oct Human-AI Interaction Design
11 7 Nov Transparent and Explainability
12 14 Nov System Quality and Inclusiveness
13 21 Nov Fairness
14 28 Nov Project Final Presentation

Credit:

The content regarding engineering aspects in this course is greatly inspired by CMU 17-445/645: Software Engineering for AI-Enabled Systems which is developed by Christian Kästner et. al.

License

Creative Commons License
Unless otherwise noted, the content of this repository is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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