AI 100, AI for Everyone, Winter 2026

Everyone is welcome to this journey to explore (at a high-level) how AI works, why it works, and when it works (or doesn't work). Our course (both on-campus and ecampus sections) is suitable for all majors and open to all students.

This course explores the basics of artificial intelligence with a non-coding approach. It explores AI's history and its wide variety of applications. It also examines current AI technologies (such as chatbots based on Generative AI) that are deployed across many fields by explaining how they work at a high level and discussing their limitations. Finally, it also examines the potential future evolution of AI.

This course also serves as the first course for all the Microcredentials on AI Fundamentals.

Coordinates [Registrar] [Canvas] [Ed Discussion]
Instructors Section 100 (on campus): Alexander Guyer (alexander.guyer@oregonstate.edu)
Section 400 (ecampus): Liang Huang (liang.huang@oregonstate.edu).
TAs Zetian Wu (wuzet@oregonstate.edu)
Office Hours TBD
Prerequisites High-school Math (Algebra 2)
Textbooks (Optional)
  • Our notes below (this course is self-contained).
  • Russell and Norvig, Artificial Intelligence, A Modern Approach, 4th edi. (2020) or 3rd edi. (2009). The definitive textbook on classical AI (Unit 2). The 4th edi. added a chapter (21) on deep learning and a chapter (24) on deep learning for language. The 3rd edi. is freely available online. All figures available online.
  • Jurafsky and Martin, Speech and Language Processing, 3rd edi. draft (2025). The definitive textbook on natural language processing (Unit 2) and deep learning (Unit 4). All chapters freely available online.
  • Mitchell, Machine Learning (1997). A classical (and outdated) textbook on machine learning, but still helpful in Unit 3. Freely available online.
Grading
  • Background survey (on Canvas): each student gets 1% by submitting on time.
  • Quizzes 1-4 (on Canvas, autograded): 6% + 5% + 10% + 10% = 26%. For each quiz, everybody has two attempts, and only the higher score is recorded.
  • HWs 1-5: 5% (fun activity) + 8% (group essay) + 15% (hands-on exploration, ML) + 20% (hands-on exploration, DL) + 20% (group hands-on exploration) =68%.
  • group/interactive components in blue (33% total).

Unit 1 (weeks 1-2): AI Overview
1.1AI History
1.2AI Paradigms
1.3AI Subfields
HW1Fun Activity using Generative AI
Quiz 1AI history, subfields, and paradigms
Unit 2 (week 3): Classical Symbolic AI
2.1AI Search and Game AI
2.2Symbolic Natural Language Processing
Quiz2Symbolic AI concepts
HW2Group Essay on Classical Symbolic AI
Unit 3 (weeks 4-5): Machine Learning AI
3.1Introduction to ML
3.2ML Settings
3.3ML Concepts
3.4\(k\) Nearest Neighbors
3.5Decision Trees
3.6Perceptron
Quiz 3machine learning concepts
HW3hands-on exploration of machine learning
Unit 4 (weeks 6-8): Deep Learning AI
4.1Multilayer Perceptron
4.2DL for vision: convolutional neural networks (CNNs)
4.3DL for language (part 1): Word Embeddings
4.4DL for language (part 2): Sequence Models: RNN, attention, Transformer, BERT, GPT
Quiz 4deep learning concepts
HW4hands-on exploration of deep learning
Unit 5 (weeks 9-10): Future of AI and AI Safety
5.1Limitations of Current AI
5.2AI Safety
5.3Future of AI
HW5group hands-on exploration of AI limitations
Liang Huang