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ai_ml_lesson.html
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ai_ml_lesson.html
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---
layout: default
title: "Placeholder Lesson"
permalink: "/classes/ml"
---
<div class="page">
<div class="content">
<div class="image-title">
<img
class="responsive-img"
src="{{site.baseurl}}/img/undraw/dev-and-teacher.svg"
alt="teacher standing in front of backboard and developer coding on laptop"
/>
<div>
<label>introduction to</label>
<h1>AI & ML</h1>
</div>
</div>
<div>
<p>
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.
</br></br>
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.
</br></br>
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.
</p>
</div>
<div class="lesson-contact">
<div>
<h3>School Location</h3>
<p>enter school location</p>
</div>
<div>
<h3>Have questions?</h3>
<p>Contact out curriculum lead!</p>
</div>
<div>
<h3>Ava Asmani</h3>
<p>[email protected]</p>
</div>
</div>
<div>
<h2>Learning goals</h2>
<div class="rounded-green-border">
<ul class="spacedlist">
<li>Understand what AI and ML are, and their differences</li>
<li>Learn about deep learning and its applications</li>
<li>Differentiate between classification and regression</li>
<li>
Use Python and related libraries in Google Colab to manipulate data
</li>
<li>Learn and apply linear regression to real-world datasets</li>
<li>Understand the intuition behind gradient descent</li>
<li>Learn and apply logistic regression to real-world datasets</li>
<li>
Understand probability, Bayes' Theorem, and binary cross-entropy loss at
a conceptual level
</li>
<li>Walk through the building blocks of a neural network</li>
<li>
Understand the challenges behind optimization and the applications of
regularization
</li>
<li>Conceptually grasp and implement convolutional neural networks</li>
<li>Explore the ethics behind applications of AI and ML</li>
</ul>
</div>
</div>
<div>
<h2>Lessons</h2>
<div class="center">
<div class="lesson-triple-col">
{% for lesson in site.aiml %}
<div class="lesson-w-chip">
<h3>{{lesson.title}}</h3>
{% if lesson.excerpt %}
{{lesson.excerpt}}
{% endif %}
<div class="green-chips">
{% if lesson.slides_link %}
<div href="{{lesson.slides_link}}">slides</div>
{% endif %}
{% if lesson.homework_link %}
<div href="{{lesson.homework_link}}">worksheet</div>
{% endif %}
{% if lesson.colab_link %}
<div href="{{lesson.colab_link}}">colab</div>
{% endif %}
</div>
</div>
{% endfor %}
</div>
</div>
</div>
<div>
<div>
<h1 class="reset">More resources</h1>
<p>These are our favorite AI/ML resources - they're all awesome!</p>
</div>
<div class="two-by-two">
{% for subcategory in site.data.curriculum_resources.aiml %}
<div>
<h3>{{subcategory.title}}</h3>
<ul>
{% for item in subcategory.items %}
<li>{{item.name}}</li>
{% endfor %}
</ul>
</div>
{% endfor %}
</div>
</div>
</div>
</div>
</div>