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COMP598_Fall2020 Software Engineering for Building Intelligent Systems

General Information

Instructor  Jin Guo
TA Breandan Considine
Class Time TR 11:35 am-12:55 pm
TA Office Hours W 11:00 am-12:00 pm
Remote Lecture Zoom (link through MyCourse)
Discussion Forum Slack
  • TODO before attending the first lecture:
  1. Please fill in this Background Survey. I will try my best to accommodate your availability, background, and expectations of the course so having the input from you is extremely important.
  2. Please join the Slack workspace and introduce yourself to the cohort. From here, we hope to know you and work with you as a collaborator over this semester.
  • Due to the COVID-19 pandemic, this course will be taught remotely. The detailed format will be updated soon.

  • I normally design many in-class activities for upper-level classes to motivate discussion and collaborative learning. Therefore, it is important to attend the lectures in order to gain the best learning experience – it cannot be replaced by watching the videos afterward. Given the special circumstance of this semester, I won't require you to attend all the lectures in real-time and try to balance the in-class and out-of-class effort. However, I would encourage you to register next year if you cannot attend most of the lectures.


This course is going to explore how to 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.


While there are no official prerequisite courses, you will enjoy and appreciate this course more if you have taken COMP303, COMP424 and COMP551 already.

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.

Assessment and Evaluation (Tentative)

Assessment Method Weight
Participation (inclass and online) 10%
Assignment 60%
Final Project 30%
  • 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 Discussant
1 3 Sep Introduction BIS book: Chapter 1, 2
TIS book: Intro (Onedrive)
2 8 Sep Intro to Modern Software Engineering GOTO 2020 Talk• Taking Back "Software Engineering"
Quality Attributes
3 10 Sep Intro to Modern Software Engineering (cont'd)
4 15 Sep AI basics (focusing on ML) BIS book: Chapter 16, 17, 18
Human Compatible: Intelligence (Onedrive)
5 17 Sep Model Selection and Evaluation BIS book: Chapter 19, 20
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Assignment1 Due (20 Sep)
6 22 Sep Model Selection and Evaluation (cont'd) Hidden Technical Debt in Machine Learning Systems Fuyuan Lyu
7 24 Sep Model -> System Software Engineering for Machine Learning: A Case Study
TIS book: Chapter 4 Why Systems Suprise Us (Onedrive)
8 29 Sep Data Acquisition & Management BIS book: Chapter 9
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
9 1 Oct Data Acquisition & Management (cont'd) Kangrui Ren
10 6 Oct Requirement for (and) AI Requirements Engineering for Machine Learning: Perspectives from Data Scientists Alexa Hernandez
R1 7 Oct Project M1 Recitation Project Intro: Movie Recommendation Breandan Considine
11 8 Oct Requirement for (and) AI (cont'd)
Human-AI Interaction Design
How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy Grace Hu
12 13 Oct Guest Lecture: Jinghui Cheng -- Designing usable machine-learning based applications Guidelines for Human-AI Interaction Younes Boubekeur
13 15 Oct Human-AI Interaction Design BIS book: Chapter 5-8
Human-Centered Artificial Intelligence: Three Fresh Ideas
Mohammad Amin Mozaffari
14 20 Oct Data Quality Assessment Data validation for machine learning
Automating Large-Scale Data Quality Verification
Project M1 Due Lucas Turpin
15 22 Oct Quality Assessment The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
BIS book: Chapter 15
Edrick Da Corte Henriquez
16 27 Oct Continuous Delivery
R2 28 Oct Project M2 Recitation Concepts in Statistical and Software Testing Breandan Considine
17 29 Oct Class Canceled Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI Assignment2 Due (29 Oct)
18 3 Nov AI principles Overview
R3 4 Nov Project M2 Recitation CI/CD tools and Practices for Effective Remote Development Breandan Considine
19 5 Nov Safety Autonomous Vehicle Safety: An Interdisciplinary Challenge
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software
Project M2 Due (12 Nov) Sogol Masoumzadeh
20 10 Nov Security and Privacy SoK: Towards the Science of Security and Privacy in Machine Learning
Designing privacy-aware internet of things applications
Yechuan Shi
21 12 Nov Accountability/Auditing Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (Section 2 and 3)
Jazlyn Hellman
22 17 Nov Transparent and Explainability Explainable machine learning in deployment
Designing Theory-Driven User-Centric Explainable AI
Eddie Cai
23 19 Nov Transparent and Explainability Jintao Guo
24 24 Nov Fairness Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI Assignment3 Due (24 Nov) Andrea Jiang
R4 25 Nov Project M3 Recitation Software Release Planning and Monitoring Breandan Considine
25 26 Nov Value in AI Design “The Human Body is a Black Box”: Supporting Clinical Decision-Making with Deep Learning
-- 3 Dec -- Project M3 Due
-- 7 Dec -- Project Final Report Due
-- 8 Dec Final Presentation


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


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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