Syllabus
Welcome to DATASCI 101! This is a non-technical introduction to artificial intelligence and how it is changing institutions, work and daily life. We will examine how modern AI systems are built and, just as much, how to question what they produce: where the data comes from, when the systems fail, and whom those failures affect. The classes combine short demonstrations (no programming required) with case studies and work on your own project. Anyone can take the course, whatever your major. The aim is a clear-eyed sense of what AI can do, and what it cannot.
Learning objectives
By the end of term, you should be able to:
- Explain in plain language the main ideas behind today’s AI systems.
- Identify the common ways these systems fail, and the data problems that cause those failures.
- Read a news article, product page or policy document about AI and assess whether its claims are credible.
- Design a small, realistic plan for an AI application: the data it requires, how you would evaluate it, and how you would limit its potential for harm.
- Consider the ethical, legal and social questions that arise when AI is deployed.
Course logistics
- Lectures: Tuesdays and Thursdays, 2:30-3:20pm.
- Location: White Hall, Room 206.
- Instructor: Danilo Freire (danilo.freire@emory.edu). Office hours by appointment; please email me to arrange a time.
- Materials: Lecture notes, tutorials, and assignment templates are on the course GitHub repository. Students must check the repository often for updates.
Prerequisites and software
There are no prerequisites. Familiarity with spreadsheets, basic statistics or introductory programming is useful but not required. We may discuss some simple Python code snippets in class, but you will not be asked to write any code.
Readings and resources
There is no single textbook. Instead, we read short papers, essays and tutorials, all linked on the course website. The syllabus may change during the term, so please check it periodically. If you would like to explore a topic in more depth, here are some books and resources I recommend:
Books
- Artificial intelligence: a guide for thinking humans by Melanie Mitchell (2020). A clear, non-technical overview of AI concepts and history.
- The master algorithm by Pedro Domingos (2015). A readable introduction to machine learning concepts and applications.
- The coming wave: technology, power, and the twenty-first century’s greatest dilemma by Mustafa Suleyman and Michael Bhaskar (2023). A widely read argument that AI and synthetic biology will be very hard to contain, written by the co-founder of DeepMind.
- The alignment problem: machine learning and human values by Brian Christian (2020). An exploration of what can go wrong when AI systems are deployed in the real world.
- AI & I: An intellectual history of artificial intelligence by Eugene Charniak (2024). A new book by a leading AI researcher, covering the history of the field from the 1950s to the present day.
- Weapons of math destruction: how big data increases inequality and threatens democracy by Cathy O’Neil (2016). A book on the dangers of unregulated mathematical models.
- Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford (2021). A critical look at the social and environmental impacts of AI technologies.
Online courses
- AI for everyone by Andrew Ng (Coursera). A non-technical introduction to AI concepts and applications. Free to audit, with a paid certificate option.
- Elements of AI by the University of Helsinki. A free, friendly introduction to AI and some of its methods.
- AI essentials by Google (Coursera). This programme is designed for people who want to gain practical AI skills for the workplace with no experience required.
- Google prompting essentials by Google (Coursera). A short course on how to effectively use and design prompts for large language models.
- Radical ideas in AI ethics by Pragmatic AI Labs (edX). A course that discusses AI through the lens of human rights and digital autonomy.
Other resources
- The AI doc: or how I became an apocaloptimist by Daniel Roher and Charlie Tyrell (2026). A light-hearted documentary featuring many of the field’s most prominent voices, with views ranging from very optimistic to very pessimistic about AI. Worth watching.
- In machines we trust. A podcast series by MIT Technology Review about the promises and perils of AI. Very accessible and engaging.
- AI for the rest of us. 25-30 minute episodes focused on explaining AI concepts to non-technical listeners.
- The gradient. A publication that features accessible articles on AI research.
- AI Now Institute. Research institute focused on the social implications of AI.
- AI for Good Lab. A Microsoft research group that highlights how AI can change society for the better.
- The AI incident database. A collection of real-world cases where AI systems have caused harm or failed.
- Teachable machine. A web-based tool by Google that allows users to create simple machine learning models without coding.
- The algorithm. A newsletter by MIT Technology Review that covers the latest developments in AI.
Assessments
Problem sets (10) — 50%. Short conceptual and practical tasks. Submit Jupyter notebooks (
.ipynb), Word documents, or PDFs. Late submissions incur a 10% penalty per day unless authorised in advance. Collaboration for discussion is allowed but answers must be written independently; list collaborators on submission.In-class quizzes (5) — 30%. Each quiz occupies a full lecture. Quizzes are open-book/open-notes and individual assessments. Discussion during quizzes is not permitted.
Final group project — 20%. Groups of 4–5 choose one of seven domains (healthcare, education, finance, law, creative industries, scientific research, or customer service), test existing AI tools in that domain, and design a new AI application. Deliverables: an optional one-page proposal (not graded), a final report of 5–10 pages, and a one-page infographic. No programming is required. Detailed instructions (PDF).
Grading scale
| Grade | A | A- | B+ | B | B- | C | D | F |
|---|---|---|---|---|---|---|---|---|
| Range | 91–100 | 86–90 | 81–85 | 76–80 | 71–75 | 66–70 | 60–65 | <60 |
Final grades are rounded to the nearest point.
Academic integrity and AI use
You are welcome to use generative AI tools such as ChatGPT, Claude or GitHub Copilot to brainstorm or draft, but any such use must be declared on your submissions. You are responsible for the correctness of your answers, and any text or code produced directly by a model must be attributed. The Emory Honour Code applies throughout.
Emory Honour Code
The Emory Undergraduate Academic Honour Code is in effect throughout the semester. The Honour Code applies to any action or inaction that fails to meet the communal expectations of academic integrity. Students should strive to excel in their academic pursuits in a just way with honesty and fairness in mind and avoid all instances of cheating, lying, plagiarizing, or engaging in other acts that violate the Honor Code. Such violations undermine both the individual pursuit of knowledge and the collective trust of the Emory community. Students who violate the Honour Code may be subject to failure of the course, a reportable record, suspension, permanent expulsion, or a combination of these and other sanctions. The Honor Code may be reviewed at: http://catalog.college.emory.edu/academic/policies-regulations/honor-code.html.
Accessibility
If you require accommodations, please contact the Department of Accessibility Services early in the term and provide their documentation. I will follow DAS guidance for all assessments, quizzes included. Please contact me privately to discuss any specific needs.