Course Syllabus
Course Instructor
Instructor: Professor David Johnson (office 3146 MEB)
Instructor contact: david.e.johnson@utah.edu
Office Hours: TBD
Class Meetings
Tuesdays and Thurdays 10:45am - 12:05pm in WEB L103. The L103 classroom is in the "lower" part of the Warnock Engineering Building and is entered through the courtyard on the west-facing side of the building. Beware of "upper" WEB on the east-facing side, where there is also a 1st floor, but not one with our classroom.
Students should come to class prepared to engage with topics and do small in-class exercises.
Textbook and Course Materials
This course will use a variety of free web resources and readings. These will be posted in the weekly Canvas modules.
Course Description and Learning Outcomes
This course provides a broad introduction to artificial intelligence (AI) as both a technological and cultural phenomenon. Students will examine the historical development of AI, key technical concepts underlying modern AI systems such as neural networks and machine learning, and emerging applications in different fields. The course explores how AI systems learn from data, generate language and images, and use human guidance through techniques like prompt design, retrieval-augmented generation, and fine-tuning. Alongside technical exploration, students will consider the social, economic, and ethical implications of AI, including issues of bias, labor disruption, authorship, and responsible use. Hands-on activities and discussions will help students critically evaluate AI’s benefits, limitations, and evolving role in society.
While there are no formal prerequisites, students should have basic math skills such as working with percentages and interpreting graphs (equivalent to MATH 1030 or above).
- Explain the core concepts and historical developments of artificial intelligence, including key milestones, technological shifts, and the rise of modern machine learning and large language models.
- Describe how contemporary AI systems process information, including tokenization, neural networks, context windows, and probabilistic generation.
- Apply foundational AI literacy skills, such as designing effective prompts, interpreting model outputs, and using basic hands-on tools for text or image generation and retrieval-augmented reasoning.
- Analyze the societal and economic impacts of AI, including its influence on labor markets, communication, creativity, and the distribution of power in technological systems.
- Evaluate ethical considerations in AI development and deployment, including fairness, bias, accountability, transparency, and responsible human oversight, and articulate informed positions in discussions or written reflections.
Grading and Assessment
Students in the class will be assessed and graded using the following policies and guidelines:
Readings The course will use weekly readings/viewings from popular science articles, blog posts, videos, and online tutorials to provide context and depth to course activities. This will be posted in the weekly Canvas modules. A small, paper, in-class quiz (most often on Tuesdays) will assess the reading assignments.
In-class activities A portion of each class will be used for small, hands-on activities designed to build intuition and understanding of lecture topics. These will often be small group activities and will assessed from a worksheet or online form. Other in-class activities will use PollEV audience response questions - see information on how to register for this under the Canvas Course Resources module.
Tests There will be two midterm, paper tests used to review and assess student understanding of material. See the course schedule below for dates.
Project The course will conclude with a final project and presentation on a topic based on course activities and student interest.
Note that many of these activities are in-class. Class attendance is essential in this course!
Grading Policies
While attendance during lecture is essential for activities such as quizzes, tests, and in-class activities, the following policies are here to allow some flexibility for life events and absences:
- Two weekly reading quizzes will be dropped
- Four in-class activity scores will be dropped
The drops are automatically applied in the Canvas gradebook and do not need to be requested.
Grade Category Weights
The course grade is computed using the following weights and grade scale.
| Category | Weight |
|---|---|
| Reading Quizzes | 25% |
| In-Class Activities | 25% |
| Midterms | 15% each (30% total) |
| Final Project | 20% |
Grade Scale
The grade scale used to map grade percentages to a letter grade is
| Percentage | Letter Grade |
|---|---|
| 94 and above | A |
| 90 to 94 | A- |
| 87 to 90 | B+ |
| 84 to 87 | B |
| 80 to 84 | B- |
| 77 to 80 | C+ |
| 74 to 77 | C |
| 70 to 74 | C- |
| 67 to 70 | D+ |
| 64 to 67 | D |
| 60 to 64 | D- |
| below 60 | E |
Course Schedule
The following table shows the expected topics each day during the semester. Some of these topics may sound highly technical - the goal of this course is to build intuition and understanding of how they work. Topics may be adjusted.
Some important dates are:
- Feb 19 Midterm 1
- April 9 Midterm 2
- Final Project Presentations Friday April 24, 10:30am - 12:30pm
|
Week |
Tuesday Topic | Thursday Topic | |
|---|---|---|---|
| Jan 5 | Introduction to AI | AI History | |
| Jan 11 | Techno-Shocks & Technological Change | AI and Jobs | |
| Jan 18 | Introduction to Machine Learning | ML Workflow + Evaluation | |
| Jan 25 | K-Nearest Neighbors | K-Means Clustering | |
| Feb 1 | N-gram Models | Markov Chains for Text | |
| Feb 8 | Neural Networks (Foundations) | Neural Networks (Training) | |
| Feb 15 | Review | Midterm 1 | |
| Feb 22 | Word Embeddings | Training Base Models | |
| Mar 1 | Modern LLMs | Tokens & Context Windows | |
| Mar 8 | No class (break) | No class | |
| Mar 15 | Retrieval-Augmented Generation (RAG) | Fine-Tuning | |
| Mar 22 | RLHF | AI Alignment | |
| Mar 29 | Image Generation (Diffusion) | Other AI Capabilities | |
| Apr 5 | Project Development + Review | Midterm 2 | |
| Apr 12 | AI Bias | AI Governance & Ethics | |
| Apr 19 | AI Futures & Speculation | No class (Finals Period) | |
| Finals Week | Friday, April 24, 2026 10:30 am – 12:30 pm |
Final Project Presentations |
Academic Policies
This course will use AI as we learn about AI. However, work that is not instructed to be done by AI must be done by you.
Read the Kahlert School of Computing's academic policiesLinks to an external site.Links to an external site. and the College of Engineering policiesLinks to an external site..
The University of Utah has resources to help you. Please also familiarize yourself with University policiesLinks to an external site. on the ADA, safety, sexual misconduct, student support, and academic misconduct.
Changes
The syllabus may be updated with reasonable notice by the instructor.