AI & ML
We present a year-long, modern machine learning class led by UCLA students! Students will discover and explore the computational and mathematical tools behind artificial intelligence and machine learning! In a single year, they will advance from understanding simple AI models that predict weather to analyzing the complex AI systems that power self-driving cars. Throughout the year, students will have numerous opportunities to learn and code with Python. Python is one of the most popular programming languages in use today, with wide-ranging applications in data processing, web development, and machine learning. Last year, our students used Python to train AI models that predict stock market prices, guess the popularity of a song on Spotify, and detect handwritten digits. Learning machine learning does not occur in a vacuum! We encourage our students to think critically about the applications and ethics of AI. In the past, we’ve brainstormed which types of AI are best-suited to tackle facial recognition and estimate house prices. Furthermore, we’ve held in-depth discussions on the ethics of AI-powered self-driving cars and what it actually means for AI to be racist.
School Location
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Have questions?
Contact out curriculum lead!
Ava Asmani
ava.asmani@gmail.com
Learning goals
- Understand what AI and ML are, and their differences
- Learn about deep learning and its applications
- Differentiate between classification and regression
- Use Python and related libraries in Google Colab to manipulate data
- Learn and apply linear regression to real-world datasets
- Understand the intuition behind gradient descent
- Learn and apply logistic regression to real-world datasets
- Understand probability, Bayes' Theorem, and binary cross-entropy loss at a conceptual level
- Walk through the building blocks of a neural network
- Understand the challenges behind optimization and the applications of regularization
- Conceptually grasp and implement convolutional neural networks
- Explore the ethics behind applications of AI and ML
Lessons
What is AI?
You’ve probably heard of Artificial Intelligence in the news. But what exactly is AI?
What is Deep Learning?
Deep learning has revolutionized AI! But, what exactly is it, and how is it different from AI and machine learning?
Supervised Learning
As mad as LeBron at JR Smith for costing the Cavaliers Game 1 Of the 2018 NBA Finals? Channel that anger into learning about supervised learning, training, and testing!
Introduction to Linear Regression
Lines are powerful! (You may take a look at the Colab now, but it will make more sense by Lesson 4d.)
Machine Learning Math
KK Slider teaching you how to multiply matrices?! Heck yeah!
Gradient Descent
You heard right! Machine learning models train by taking hikes down steep mountains. After all, the decision trees are quite beautiful.
Linear Regression w/ TensorFlow
Lines are even more powerful when you know TensorFlow!
Logistic Regression
Let’s train a model that can classify an image as being of either a dog or cat!
Bayes’ Theorem
Hey, baes, let’s leverage Bayes’ Theorem to build a spam filter!
Binary Cross-Entropy Loss
Using probability as a shovel, we’ll dig a little deeper into binary cross-entropy loss (you know, the thing that we optimize to train logistic regression models).
Fully-Connected Neural Networks
Ooh, let’s demystify neural networks! (And, no, they do not work like the brain does.)
Optimization & Regularization
Gradient descent is throwing a party, and we’re all invited! Get ready to meet its family and friends.
Convolutional Neural Networks
Images, filters, and networks, oh my! We’ll break down convolutional neural networks without any convolutions. (slides are in progress)
More resources
These are our favorite AI/ML resources - they're all awesome!
ACM AI Resources
- You Belong in AI! Podcast
- ACM AI Blog
- Plotting Data Notebook
- MNIST Dataset with Keras Notebook (Archer School Event)
Intro to Deep Learning
- Coursera: Deep Learning Specialization
- The Deep Learning Textbook
- 3Blue1Brown: Neural Networks
- Udacity: Intro to Deep Learning with TensorFlow
Tensorflow and Pytorch
- UC Berkeley: CS294 Tensorflow Tutorial
- UC Berkeley: CS285 (Deep Reinforcement Learning) Resources
- Pytorch: Getting Started with Pytorch
Intro to Machine Learning
- Coursera: Introduction to Machine Learning
- Hal Daumé III: A Course in Machine Learning
- Google: Machine Learning Crash Course