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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>CME 193 - Scientific Python</title>
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</head>
<body>
<header class="navbar navbar-default navbar-fixed-top">
<a class="navbar-brand" href="./">
CME 193 - Scientific Python
<small class="hidden-xs hidden-sm">
Stanford University
</small>
</a>
</header>
<main class="container-fluid">
<div class="row">
<nav id="sidebar" class="col-sm-3 col-lg-2" role="navigation">
<p class="text-muted">
Course Information
</p>
<ul class="nav nav-pills nav-stacked">
<li class="">
<a href="./index.html">
Course Description
</a>
</li>
<li class="active">
<a href="./syllabus.html">
Syllabus
</a>
</li>
<li class="">
<a href="./lectures.html">
Lectures
</a>
</li>
<li class="">
<a href="./homework.html">
Homework
</a>
</li>
</ul>
<p class="text-muted">
Other Links
</p>
<ul class="nav nav-pills nav-stacked">
<li class="">
<a href="./getstarted.html">
Getting Started
</a>
</li>
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Jupyter
</a>
</li>
</ul>
</nav>
<section class="col-sm-offset-3 col-lg-offset-2 col-sm-9 col-lg-10">
<h1 id="syllabus">Syllabus</h1>
<p>This is a course on scientific computing using Python. We'll cover aspects of the Python language as they are relevant to the material. The following schedule should be seen as a high-level guide to what we'll do in 8 lectures, but is not set in stone.</p>
<ol>
<li>Introduction to Python & NumPy</li>
<li>Dense Linear Algebra in NumPy</li>
<li>Intro to SciPy - Dense & Sparse linear algebra</li>
<li>Optimization I - Scipy.optimize</li>
<li>Pandas, Sckit learn</li>
<li>Tensorflow</li>
<li>Optimization II - ortools</li>
<li>TBD (Survey)</li>
</ol>
<p>We'll intersperse the visualization libraries:</p>
<ul>
<li>Matplotlib</li>
<li>Plotly</li>
</ul>
</section>
</div>
</main>
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