index.html

---
layout: default
title: Home
weight: 0
---

<section class="main-container page-head">
	<div class="main">
		<h1 class="head-title">CS 181: Machine Learning (2022)</h1>
		<p class="head-subtitle">Harvard University</p>
		<p class="head-subtitle">Prof. Finale Doshi Velez</p>
		<p class="head-subtitle">Time: TTh 9:45-11:00am</p>
		<p class="head-subtitle">Location: TBA</p>
	</div>
</section>
<section class="main-container text">
	<div class="main">
		<h2 class="title announcement">Announcements</h2>
		<ul>
			<li> Classes begin Monday, January 24, 2022. </li>
		</ul>
		</p>
	</div>
</section>
<section class="main-container text">
	<div class="main">
		<h2 class="title">About</h2>
		<p>
			CS 181 provides a broad and rigorous introduction to machine learning, probabilistic reasoning 
			and decision making in uncertain environments. We will discuss the motivations behind common 
			machine learning algorithms, and the properties that determine whether or not they will work well 
			for a particular task. You will derive the mathematical underpinnings for many common methods, 
			as well as apply machine learning to challenges with real data. In doing so, our goal is that you 
			gain a strong conceptual understanding of machine learning methods that can empower you to pursue 
			future theoretical and practical directions.

      		Topics include: supervised learning, ensemble methods and boosting, neural networks,
			support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood,
			graphical models, hidden Markov models, inference methods, reinforcement learning.
		</p>
		<p>
			The material is aimed at an advanced undergraduate level. Students should be comfortable 
			with writing non-trivial programs (e.g., CS 51, CS 61, or equivalent). All staff-provided 
			code will be in Python. Students should also have a background in probability theory 
			(e.g., STAT 110 or equivalent), and familiarity with calculus and linear algebra 
			(e.g., AM 22a or Math 21ab, or equivalent).
			
		</p>
		<p class="link">
			<a href="{{ site.baseurl }}/syllabus">Full course description with policies.</a>
		</p>
	</div>
</section>