[COMP585_Fall2023] Intelligent Software Systems

The syllabus is mostly inherited from the previous years. Will be updated until the start of the semester.

General Information


Instructor	Jin Guo
TA	Deeksha Arya
Class Time	MW 01:05 pm-02:25 pm
Location	ENGMC 103
Discussion Forum	Slack (link on MyCourses)

The lectures consist of many in-class activities to motivate discussion and collaborative learning. In-person participation is required.
Occasionally, if there are unforeseen reasons that prevent you from joining the lectures in person, we will try to accommodate. Please contact us prior to the lecture.

Description

This course is going to explore how to design and build an intelligent system from a software engineering perspective, from requirement gathering and analysis to deployment and maintenance. We will also touch AI ethics and its implications to design.

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 later.

Books:
- Thinking in Systems: A Primer (Chapter scans will be shared through OneDrive links)
- Human Compatible: AI and the Problem of Control (Chapter scans will be shared through OneDrive links)
- Design Justice (open access)
- Building Intelligent Systems A Guide to Machine Learning Engineering (access through McGill Library)

Assessment and Evaluation (Tentative)

Assessment Method	Weight
Participation	10%
Reading Reports	10%
Assignment	45%
Final Project	35%

Any form of plagiarism, cheating is strictly banned during midterm or final exam. 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.

Schedule (Tentative)

Subject to adjustments

Lecture	Date	Content	Reading	Note
1	31 Aug	Introduction	BIS book: Chapter 1, 2 TIS book: Intro (Onedrive)
2	7 Sep	Intelligent Systems and Software Engineering	Human Compatible: Intelligence (Onedrive) Quality Attributes
3	12 Sep	Lecture Canceled		Assignment 1 (Due Sept 19th)
4	14 Sep	Project Meetup		Project M1 (Due Oct 2nd))
5	19 Sep	ML Model Quality	BIS book: Chapter 19, 20 Beyond Accuracy: Behavioral Testing of NLP Models with CheckList Model Cards for Model Reporting
6	21 Sep	From Model to System	Software Engineering for Machine Learning: A Case Study Hidden Technical Debt in Machine Learning Systems TIS book: Chapter 4 Why Systems Suprise Us (Onedrive)
7	26 Sep	Data Acquisition & Management	BIS book: Chapter 9 A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
8	28 Sep	Requirement and AI	Requirements Engineering for Machine Learning: Perspectives from Data Scientists Keynote talk by Amy Ko at RE 2021
9	3 Oct	No Class (Election Day)
10	5 Oct	Requirement and AI (cont.) Team and Collaboration	Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process Data Scientists in Software Teams:State of the Art and Challenges
11	13 Oct (READING WEEK MakeUp Class)	M1 Presentation Team and Collaboration (cont.)		Project M2 (Due Oct 28th))
12	17 Oct	Data Quality	Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI
13	19 Oct	System Quality and CD	BIS book: Chapter 15 The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
14	24 Oct	Guest Lecture by Jazlyn Hellman - Human Centered Design
15	26 Oct	Human Centered Design (cont.)
16	31 Oct	Design for Human-AI Interaction	Guidelines for Human-AI Interaction Human-Centered Artificial Intelligence: Three Fresh Ideas	Assignment 2 (due Nov 9th)
17	2 Nov	M2 Presentation		Project M3 (Due Nov 24th))
18	7 Nov	Design - Decision-making	The Principles and Limits of Algorithm-in-the-Loop Decision Making Judgment under uncertainty: Heuristics and biases
19	9 Nov	Transparent and Explainability	Explainable machine learning in deployment Designing Theory-Driven User-Centric Explainable AI
20	14 Nov	Inclusive Design	Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors Design Values: Hard-Coding Liberation?
21	16 Nov	Fairness	The (Im)possibility of fairness: different value systems require different mechanisms for fair decision making Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
22	21 Nov	Lecture Canceled
23	23 Nov	Creativity	Creativity support tools: accelerating discovery and innovation AI as Social Glue: Uncovering the Roles of Deep Generative AI during Social Music Composition
24	28 Nov	Security	SoK: Towards the Science of Security and Privacy in Machine Learning The AI-Based Cyber Threat Landscape: A Survey	Assignment 3 (due Dec 5th)
25	30 Nov	Privacy and Wrap Up	Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI
26	5 Dec	Presentation

Credit:

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

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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