15-181, New in Spring 2022!

Demystifying Artificial Intelligence


Key Information

Tuesday + Thursday, 10:10 am - 11:30 am, DH 1112

Online, via Zoom, for the first two weeks. See Piazza for Zoom links.

Series of quizzes, final exam, homework exercises, and a series of mini-projects to learn about how AI systems work and how they can become biased and problematic.

Grades will be collected in Canvas.
Quizzes 25%, Final 15%, Assignments 50%, Participation 10%

We will use Piazza for questions and any course announcements.

Students will turn in their homework electronically using Gradescope.

This course will pull back the curtains on artificial intelligence, helping you learn what it is, what it can do, how to use it, how it works, and what can go wrong. This course is designed for students that want to learn about AI and machine learning but don't have the course schedule bandwidth to build up the math and computing background required for full-fledged intro AI and ML courses, such as 15-281 and 10-301. Leveraging high school algebra and basic Python programming skills from 15-110, we'll help you implement key pieces of AI techniques from the nearest neighbor algorithm to simple neural networks. Through in-class activities, weekly recitations, and course assignments, you'll start to learn how to use AI systems, including how to make them "intelligent", what data might be needed, and what can go wrong. Ethical discussions will be woven throughout the course to enable you to think critically about how AI impacts our society.


The prequisite for this course is:

  • 15-110 Principles of Computing (15-112 is also acceptable)

That's it! Normally, AI and Machine Learning courses require prereqs for probability, linear algebra, calculus, and 15-122. We'll build on 15-110 computing skills and basic high school algebra.

Who should take this course?

This course is particularly good for students that want to learn AI but aren't planning to take the list of AI/ML course prerequisites: 15-122 + probability + linear algebra, etc.

Why this course?

As artificial intelligence technology becomes more pervasive in both everyday products and mainstream media, it is increasingly important that all of us begin to learn about the basic concepts behind AI. While computer science, calculus, linear algebra, and probability prerequisites are required to become an AI developer, students outside of the SCS and STEM majors can learn a myriad of machine learning and AI concepts by simply leveraging the your knowledge of high school algebra and basic programming constructs.

Office Hours

If standing office hours don't work for you, we're happy to do office hours by appointment. See available appointment slots or make a private post on Piazza with a few times that work for you and we'll set something up.

Schedule (subject to change)

Date Topic Slides Notes/Code
1/18 Tue 1: Overview & Intelligence pptx (inked) pdf (inked)
1/20 Thu 2: Five Big Ideas in AI & Agents pptx (inked) pdf (inked)
1/25 Tue 3: Visualizing Simple Data N/A visualizing_data_1.ipynb
1/27 Thu 4: Visualizing Simple Data N/A visualizing_data_3.ipynb
2/1 Tue 5: Nearest Neighbor Classification pptx (inked) pdf (inked) nearest_neighbor_1.ipynb
2/3 Thu 6: Data & ML Models pptx (inked) pdf (inked)
2/8 Tue 7: Image Classification pptx (inked) pdf (inked)
2/10 Thu 8: Design Challenges pptx (inked) pdf (inked)
2/15 Tue 9: Linear Regression and Optimization pptx (inked) pdf (inked) regression_interactive.ipynb
regression_2_grid_search.ipynb (sol)
2/17 Thu 10: Linear Regression and Optimization see prev slides regression_3_search_visualization.ipynb (sol)
gradients.ipynb (sol) (sol N-D)
regression_optimization.ipynb (sol)
2/22 Tue 11: Regression with More Features see prev slides coins.ipynb (sol)
2/24 Thu 12: Regression Applications pptx (inked) pdf (inked)
3/1 Tue 13: Neuron for Non-linear Datasets pptx (inked) pdf (inked)
3/3 Thu 14: Three Neuron Network see prev slides
3/8 Tue No class: Spring Break
3/10 Thu No class: Spring Break
3/15 Tue 15: Neural Network Optimization pptx (inked) pdf (inked)
3/17 Thu 16: Neural Network Structure pptx (inked) pdf (inked)
3/22 Tue 17: Using Neural Networks see prev slides
3/24 Thu 18: Feature Learning: Dimensionality Reduction pptx (inked) pdf (inked)
3/29 Tue 19: Feature Learning: Autoencoders see prev slides
3/31 Thu 20: GANs pptx (inked) pdf (inked)
4/5 Tue 21: GANs see prev slides
4/7 Thu No class: Carnival
4/12 Tue 22: Natural Language Processing pptx (inked) pdf (inked)
4/14 Thu 23: Natural Language Processing see prev slides
4/19 Tue 24: Search Applications pptx (inked) pdf (inked)
4/21 Thu 25: Search Trees see prev slides
4/26 Tue 26: Constraint Satisfaction Problems see prev slides
4/28 Thu 27: Human Compatible AI pptx (inked) pdf (inked)
5/3 Tue FINAL EXAM Take home exam - 72 hours 5/3 Tue 5:30 pm - 5/6 Fri 5:30 pm