Welcome to CS 375

AI / Machine Learning
Spring 2026

Ken Arnold

As You Enter…

Introduce yourself to a few people around you. Try to meet someone you don’t know yet. Discuss with your neighbors:

• What about AI are you hopeful about?
• What about AI are you concerned about?
• How might AI connect with Christian faith?

Each person should write one of each on the whiteboard. (Or add a star or “+1” next to something already there.)

My Story

• How God brought me to learn about ML/AI
• Cornell
• MIT Media Lab (+humanism)
• Luminoso
• Harvard
  • organizing things
  • writing and predictive text
• “Do justice with tech”?

My Stance

• AI is a gift that will be in the new creation
• But we abuse it

We Need to Discern AI Together

Divisiveness

• So many different tools / technologies
• So many different uses

Economic Impacts

• Economy is betting heavily on AI

Existential Angst

• Our problems are so big we need AI to help solve them.
• But it’ll kill us all!

Identity, Desires, and Relationships

• Do I want to interact with a machine or a person?
• What’s worth doing / thinking about? (vs automating)
• What’s actually good? Can “good” be benchmarked?
• What do we desire?
• What will we build (now that we can create by prompting)?
• What values will we instill?

We shape our tools, and thereafter our tools shape us. Winston Churchill, as adapted by Marshall McLuhan

Discerning Fundamentally

• You need to be able to discern it fundamentally, not just from external behavior

This Class

• How AI works at a fundamental level
• What that understanding helps us see about how it fits into God’s story

Tweakable Machines Playing Optimization Games

• Board games
• Hook-the-human games
• Predict protein folding, guess the weather, design a molecule, …
• Imitation games: mimicking decisions, conversations, images, …
• Exploration games: control a robot, …

Problem Framing

Programmed vs Learned

Supervised Learning

• Mimicry

Self-Supervised Learning

• Reducing surprise

Reinforcement Learning

• Learning by trial and error

Objectives, colloquially

You are effective at doing what’s just and right with AI.

So, you should be:

• self-directed, collaborative learners
• able to be immediately productive at some kinds of ML problems
• in the habit of practicing the integrity and perseverance needed to work in a team

Minimal Logistics

• Discussion 1
• Reading (Perusall)

Logistics

Standards-Based Grading

• Why: to focus on learning, trust you, and give you flexibility.
• Details: let’s look at the syllabus together.

Notes:

• The Moodle gradebook is not yet set up to reflect this system. (I’m working on it.)
• I’m going to get this wrong the first time. Feedback is welcome!

Textbook

• Primary textbook: Deep Learning with Python, by François Chollet
  • if you buy the print version from Manning; you get the ebook for free
• Supplemental material from:
  • Understanding Deep Learning by Simon J.D. Prince (free online)
  • Deep Learning - Foundations and Concepts by Chris Bishop (free online, awkwardly)

Tech Stack

• Kaggle Notebooks (for running code on GPUs)
• PyTorch and TorchVision
• Hugging Face Transformers (for NLP and some CV)
• OpenAI API (for accessing commercial models)

AI Policy

• Use all types of AI maximally to help you learn.
  • Ask for analogies for complex concepts
  • Have it ask you questions to check your understanding
  • Talk about relationships between concepts
  • Use it to help write code
  • …
• Share your strategies with others (and we’ll discuss some together)

But:

• I don’t want to read any “slop”. (Show me your prompt instead!)
• Beware cognitive disengagement (the “feeling of learning”, thinking you understand because you can follow along)