Fall 2025 Teaching
CS 263 – AI in the World
https://aaz-imran.github.io/teaching/2025-fall-cs263
Syllabus
Time: Tuesdays and Thursdays 2:00 pm – 3:15 pm
Location: Register course to know
Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Tuesdays 11 am-1 pm
Course Description:
This course is intended to be accessible to all first-year undergraduates and those in other years. It is not about the technical details of AI systems, but rather is about what AI is, what it does and doesn’t do, and what it should and shouldn’t do, what its role and impact are on society. The topics covered in this course will be: History of AI, Historical AI Categories of current AI systems, Practice in use of current AI systems, Ethical considerations in the design and use of AI systems, Understanding the social context of AI (socio-technical systems).
Course Outcomes
At the end of the course, the students will be able to:
- Define current categories of AI (e.g., expert systems, supervised vs. unsupervised machine learning, multi-agent systems) and their limitations and advantages
- Understand the differences between human reasoning and AI
- Apply ethical frameworks or notions of bias and fairness in machine learning to reason about current uses of AI
- Analyze current AI in the language of sociotechnical systems
- Evaluate social and legal policies about AI in terms of implicit and explicit value systems, and in terms of effectiveness
- Accomplish creative work aided by appropriate AI tools
Course Schedule/Outline (Tentative):
| Week | Topics | Suggested Reading | Note |
|---|---|---|---|
| 1. | Aug 26: Course Introduction Aug 28: History and Goals of AI | ||
| 2. | Sep 2: Classical AI Sep 4: Logic Programming | ||
| 3. | Sep 9: Expert Systems Sep 11: Multi-Agent Systems | release of hw1 | |
| 4. | Sep 16: Basics of Machine Learning Sep 18: Learning Paradigms | ||
| 5. | Sep 23: Deep Learning Basics Sep 25: Deep Learning Tools and Usage | hw1 due | |
| 6. | Sep 30: Generative AI Oct 2: Generative AI Tools and Usage | ||
| 7. | Oct 7: Data, Information, and Knowledge I Oct 9: Data, Information, and Knowledge II | project proposal due | |
| 8. | Oct 14: Bias and Fairness Oct 16: Trustworthy AI | release of hw2 | |
| 9. | Oct 21: Midterm Review Oct 23: Midterm Exam | ||
| 10. | Oct 28: No Lecture – Fall Break Oct 30: Project discussion | project milestones due | |
| 11. | Nov 4: Ethical Frameworks Nov 6: Ethics in AI | hw2 due | |
| 12. | Nov 11: [Debate] “Should important life decisions be made by AI or by humans?” Nov 13: AI in Legal Practice | release of hw3 | |
| 13. | Nov 18: Intro to Socio-Technical Systems Nov 20: AI for Health | ||
| 14. | Nov 25: Climate Change AI Nov 27: AI for Privacy Preservation | hw3 due | |
| 15. | Dec 2: Scientific Discovery with AI Dec 4: AI for Equality and Education | ||
| 16. | Dec 9: Prep Day – Final Discussion and Review Dec 11: Reading Day – No Lecture | ||
| 17. | Dec 16: Final Presentation | Report due |
Course Activities:
- Class participation (10%) – lecture quizzes, in-class and Canvas activities
- Debate (10%) - in-class debate on a preselected topic
- Assignments (30%) – Three written assignments
- Project/Paper (30%) – Final group project (final presentation and report)
- Assessments (20%) – In-class Exam
Grading Scale:
After the final percentage grade is calculated, the following scale will be used to determine the final letter grade.
- 90–100% (A)
- 80–89% (B)
- 70–79% (C)
- 60–69% (D)
- 0–59% (E)
READ THIS:
Attendance Policy: Students are required to attend every lecture. Students only present in the class can take the associated quiz and get the available points. Missing a maximum of 2 lectures will be excused. Any student missing more than 2 lectures without any reasonable excuses will start losing 10 points for every missed lecture.
Academic Integrity: Please strictly follow the Academic Offenses Rules (plagiarism, cheating, and falsification or misuse of academic records). Also, keep in mind that any copyrighted materials (e.g., images and other media), and published contents (e.g., academic papers, books, web sources, online tools) used in your submissions and project should be properly cited. Ideas from people other than your own (for the project—ideas from outside your group) should be acknowledged.
Late Policy: Late submissions (assignment, project proposal, code, project final report) will be subject to a 25% score penalty per day after the deadline. A score of 0 will be given for any submissions after four days past the deadline. Late submissions will be accepted only for certain circumstances (e.g., medical, religious) with proper evidence.
Exceptions to this rule may be requested by providing appropriate documentation which will be considered at the discretion of the instructor.
Generative AI Policy: GenAI tools such as ChatGPT may be used in this course for pre-submission activities. However, students are responsible for submitting work that meets the assignment standards for quality and factual accuracy. Check the Canvas page for more detailed instructions on this. If you have any questions or concerns about the policy, contact the instructor before submitting any assignments.
Disability and Special Accommodation: Please let the instructor know of any needed accommodation in the first two weeks. Please also see Academic Accommodation for further assistance.
Academic Policy Statements, Resources Available to Students
Useful Resources: