# Introduction to AI & ML

Class & Curriculum Outline

Learn machine learning, with us! A joint collaboration with ACM AI Outreach.

## Brief Overview

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.

## Lessons

### #1 What is AI?

You’ve probably heard of Artificial Intelligence in the news. But what *exactly* is AI?

### #2 What is Deep Learning?

Deep learning has revolutionized AI! But, what *exactly* is it, and how is it different from AI and machine learning?

### #3 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!

### #4a 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.)

### #4b Machine Learning Math

KK Slider teaching you how to multiply matrices?! Heck yeah!

### #4c Gradient Descent

You heard right! Machine learning models train by taking hikes down steep mountains. After all, the decision trees are quite beautiful.

### #4d Linear Regression w/ TensorFlow

Lines are even more powerful when you know TensorFlow!

### #5 Logistic Regression

Let’s train a model that can classify an image as being of either a dog or cat!

### #6 Bayes’ Theorem

Hey, baes, let’s leverage Bayes’ Theorem to build a spam filter!

### #7 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).

### #8 Fully-Connected Neural Networks

Ooh, let’s demystify neural networks! (And, no, they do not work like the brain does.)

### #9 Optimization & Regularization

Gradient descent is throwing a party, and we’re all invited! Get ready to meet its family and friends.

### #10 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