Overview

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.

Prerequisites

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.

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 visualizing_data_2.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 nearest_neighbor_2.ipynb nearest_neighbor_3.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_1.ipynb regression_blind_optimization.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_blind_optimization_gradients.ipynb 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