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<!DOCTYPE HTML>
<!--
Stellar by HTML5 UP
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Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
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<html>
<head>
<title>Machine Learning@MSU</title>
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<h1>Machine Learning @ MSU</h1>
<p><a href="http://jiayuzhou.github.io" target="_blank">Professor Jiayu Zhou</a> from Michigan State University is offering introductory (CSE 491) and advanced (CSE 847) courses on machine learning.</p>
</header>
<!-- Nav -->
<nav id="nav">
<ul>
<li><a href="#intro" class="active">Introduction</a></li>
<li><a href="#first">Learning Outcomes</a></li>
<li><a href="#second">Topics</a></li>
<li><a href="#cta">Course Projects</a></li>
<li><a href="#enroll">Enroll</a></li>
<li><a href="#research">Research</a></li>
</ul>
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<!-- Main -->
<div id="main">
<!-- Introduction -->
<section id="intro" class="main">
<div class="spotlight">
<div class="content">
<header class="major">
<h2>What is machine learning?</h2>
</header>
<p>
We are now living in the era of big data, where new information of various types is being acquired and stored every second. The massive amount of data provides us insights to improve many applications, from science areas such as medical informatics, bioinformatics, to integrated solutions such as artificial intelligence or smart cities. On the other hand, it has also imposed challenges in the data analysis. Machine learning is the key to tackle these challenging data science issues, integrating techniques from mathematics and computer science in a principled way, and providing systematical approaches to analyze large-scale datasets.
</p>
<p>
As a computer science field, machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., that learn to spot high-risk medical patients, recognize speech, classify text documents, detect credit card fraud, or drive autonomous robots). This course provides an in-depth understanding of machine learning and statistical pattern recognition techniques and their applications in biomedical informatics, computer vision, and other domains.
</p>
<ul class="actions">
<li><a href="https://en.wikipedia.org/wiki/Machine_learning" target="_blank" class="button">Learn More</a></li>
<li><a href="#enroll" class="button special">Enroll</a></li>
</ul>
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<span class="image"><img src="images/ml_pictogram3.png" alt="" /></span>
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</section>
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<section id="first" class="main special">
<header class="major">
<h2>Learning Outcomes</h2>
</header>
<ul class="features">
<li>
<span class="icon major style1 fa-university"></span>
<h3>Learning Foundations</h3>
<p>Understanding the foundation, major techniques, applications, and challenges of machine learning.</p>
</li>
<li>
<span class="icon major style3 fa-line-chart"></span>
<h3>Applied Machine Learning</h3>
<p>The ability to implement and apply basic machine learning algorithms for solving real-world problems. </p>
</li>
<li>
<span class="icon major style5 fa-code"></span>
<h3>Advanced Machine Learning</h3>
<p>(Graduate-level) The ability to develop new machine learning algorithms tailored to specific applications.</p>
</li>
</ul>
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<header class="major">
<h2>Topics Covered</h2>
<p>Background</p>
</header>
<p align="left">Machine learning is an interdisciplinary direction that builds the intersection among many mathematical topics and computer science foundations. To make the course self-contained relevant math topics will be briefly reviewed in the lectures, but it strongly recommended that students took these subjects formally before enrolling. Currently <a href="https://en.wikipedia.org/wiki/Python_(programming_language)" target="_blank">Python</a> is used in CSE 491 because of its popularity in industries. <a href="https://en.wikipedia.org/wiki/MATLAB" target="_blank">Matlab</a> is used in CSE 847 so students are focused on learning algorithms. Note that Matlab is <a href="https://techstore.msu.edu/software/mathworks-matlab-student-license-0" target="_blank">free</a> for current MSU students. In addition, the graduate level course requires the use of <a href="https://en.wikipedia.org/wiki/LaTeX">Latex</a> and <a href="https://github.com" target="_blank">Github</a>. The students are expected to be familiar with the following topics:</p>
<ul class="statistics">
<li class="style1">
<a href="https://en.wikipedia.org/wiki/Probability" target="_blank">Probability</a> and <a href="https://en.wikipedia.org/wiki/Statistics" target="_blank">Statistics</a>
</li>
<li class="style2">
<a href="https://en.wikipedia.org/wiki/Linear_algebra" target="_blank">Linear Algebra</a> Basics
</li>
<li class="style3">
Numerical <a href="https://en.wikipedia.org/wiki/Mathematical_optimization" target="_blank">Optimization</a>
</li>
<li class="style4">
<a href="https://en.wikipedia.org/wiki/Python_(programming_language)" target="_blank">Programming Languages</a>
</li>
</ul>
<header class="major">
<p>Machine Learning Foundations</p>
</header>
<p align="left">Even though machine learning models nowadays become increasingly complicated, simple models such as linear ones are still the core components of machine learning research. Not only do they offer theoretical foundations of and insights into the more complicated models, their performance remains powerful in practical applications. We will cover the following:</p>
<ul class="statistics">
<li class="style1">
Linear Regression
</li>
<li class="style2">
Linear Classification
</li>
<li class="style3">
Support Vector Machines
</li>
<li class="style4">
Tree Methods
</li>
<li class="style5">
Unsupervised Learning
</li>
</ul>
<p align="left">The theoretical behavior of these methods is thoroughly studied in the past. They are easy to implement and deploy, and they are among the first to try when solving real-world problems.</p>
<header class="major">
<p>Advanced Machine Learning Topics</p>
</header>
<p align="left">In addition, the graduate course CSE 847 includes overviews of advanced machine learning topics from cutting edge academic and industry machine learning research. The topics include but not limited to the following: </p>
<ul class="statistics">
<li class="style3">
Advanced Linear Algebra (e.g., SVD)
</li>
<li class="style4">
Sparse Learning, Matrix Completion
</li>
<li class="style1">
Ensemble Methods
</li>
<li class="style2">
Multi-task and Transfer Learning
</li>
<li class="style5">
Neural Networks and Deep Learning
</li>
</ul>
<p align="left">These are active research topics, and graduate students can identify areas that align with their research interests and existing projects.</p>
<footer class="major">
<ul class="actions">
<li><a href="https://github.com/jiayuzhou/CSE491-2016Fall" target="_blank" class="button">CSE491 Syllabus</a></li>
<li><a href="https://github.com/jiayuzhou/CSE847/blob/master/syllabus/Syllabus-2018Spring.md" target="_blank" class="button">CSE847 Syllabus</a></li>
</ul>
</footer>
</section>
<!-- Get Started -->
<section id="cta" class="main special">
<header class="major">
<h2>Course Project Gallery</h2>
<p>In the graduate level machine learning course, the students will have chance to
form teams working towards research projects of choice. Here are a few examples: </p>
</header>
<p align="left"><span class="image left"><img src="images/proj_ci.png" alt="" /></span>In this project, the team proposed and evaluated different approaches to automatically generate Chinese poems (Ci). Ci is one of the most important genres of Chinese classical poetry. As a precious cultural heritage, not many of them have been passed down onto the current generation. Therefore, the study of automatic generation of Ci is meaningful, not only because it supplements entertainment and education resources to modern society, but also because it demonstrates the feasibility of applying artificial intelligence in Art generation.
<a href="https://github.com/msu-ml/17spr_wang_zhu_du" target="_blank" >Learn More</a></p><br/>
<p align="left"><span class="image left"><img src="images/proj_deep.png" alt="" /></span>In this project the team studied the object tracking work of Hong, et al. 2015, and reproduced their work through the creation of a viable demo. The study investigates how to solve challenges of occlusion, pose variations, illumination changes, fast motion, and background clutter. <a href="https://github.com/msu-ml/16spr_gonzales_hoffman" target="_blank">Learn More</a></p><br/>
<p align="left"><span class="image left"><img src="images/proj_gwas.png" alt="" /></span>In this project the team seeks to predict complex human phenotype from high dimensional whole genome profiles. To solve the curse of dimensionality, they explored a novel two-tier modeling by first choosing representative features from chromosome blocks, then build a higher tier predictive model. They showed improvements of predictive accuracy over existing GWAS or kernel based models. <a href="https://github.com/msu-ml/17spr_tong_sun" target="_blank">Learn More</a></p><br/>
<p align="left"><span class="image left"><img src="images/proj_avm2.jpg" alt="" /></span>Automated Valuation Models (AVM) have become increasingly popular as the real estate market has embraced the World Wide Web as a source of accurate, up to the minute data. Banks have also shown great interest in using AVMs to help mitigate fraud by human appraisal. The team explored various machine learning techniques to implement an AVM and predicted the true value of a house based on features commonly found on real estate listings. <a href="https://github.com/msu-ml/17spr_lingg_langford_lucero" target="_blank">Learn More</a></p><br/>
<footer class="major">
<ul class="actions">
<li><a href="https://github.com/msu-ml" target="_blank" class="button special">Explore the Class Repo</a></li>
</ul>
</footer>
</section>
<!-- Enroll -->
<section id="enroll" class="main special">
<header class="major">
<h2>Join the Machine Learning Journey</h2>
<p>The undergraduate level machine learning course CSE 491 is offered in Fall semesters<br />
semesters. The graduate level course CSE 847 is offered in Spring semesters.</p>
</header>
<div class="row uniform">
<div class="12u$"><span class="image fit"><img src="images/classphoto.jpeg" alt="" /></span></div>
</div>
<div class="row uniform">
<div class="12u$"><span class="image fit"><img src="images/classphoto2.jpg" alt="" /></span></div>
</div>
<br/>
<p>Check out the quotes from students' feedback:</p>
<blockquote>Very comprehensive, my favorite CSE elective. <br/> -- Anonymous student from CSE 491</blockquote>
<blockquote>Loved the implementation of models after learning the theory. <br/> -- Anonymous student from CSE 491</blockquote>
<blockquote>The course covered much depth and width of literature and many frontier advances. This course will greatly assist my career. <br/>-- Anonymous student from CSE 847</blockquote>
<blockquote>Very interesting course, loved the applications. <br/>-- Anonymous student from CSE 847</blockquote>
<footer class="major">
<ul class="actions">
<li><a href="https://schedule.msu.edu" class="button" target="_blank">Check Schedule</a></li>
<li><a href="https://schedule.msu.edu" class="button special" target="_blank">Enroll</a></li>
</ul>
</footer>
</section>
<!-- Enroll -->
<section id="research" class="main special">
<header class="major">
<h2>Interested in Machine Learning Research?</h2>
<p>The <a href="http://illidanlab.github.io/" target="_blank"><b><font color="#0db14b">Intelligent Data Analytics (ILLIDAN) lab @ MSU</font></b></a> is
conducting cutting-edge machine learning
research for big data analytics. ILLIDAN lab designs scalable machine learning algorithms, creates open source machine learning software, and develops powerful machine learning for applications in health informatics, big traffic analytics, computational finance and other scientific areas.<br />
You can affiliate with ILLIDAN lab in the form of doctoral/post-doctoral training, M.S. thesis, hourly-paid researchers, or research volunteers. Please contact the lab head <a href="http://jiayuzhou.github.io/" target="_blank">Dr. Jiayu Zhou</a> for openings.
</p>
</header>
<footer class="major">
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<li><a href="http://jiayuzhou.github.io/" class="button" target="_blank">Lab Head</a></li>
<li><a href="http://illidanlab.github.io/" class="button special" target="_blank">Learn More</a></li>
</ul>
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</section>
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<!-- Footer -->
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<section>
<h2>Acknowledgment</h2>
<p>The development of course materials is supported in part by
National Science Foundation through grants <a href="http://www.nsf.gov/awardsearch/showAward?AWD_ID=1565596" target="blank">IIS-1565596</a> and <a href="http://www.nsf.gov/awardsearch/showAward?AWD_ID=1615597" target="blank">IIS-1615597</a>. The teaching of course is supported by
Github through free git repositories for students. </p>
<p><img width="50px" src="images/nsf.png"></p>
</section>
<section>
<h2>Instructor - <a href="http://jiayuzhou.github.io/" target="_blank">Dr. Jiayu Zhou</a></h2>
<dl class="alt">
<dt>Address</dt>
<dd>429 S Shaw Ln • East Lansing, MI 48823 • USA</dd>
<dt>Phone</dt>
<dd>(517) 353-4389</dd>
<dt>Email</dt>
<dd><a href="#">[email protected]</a></dd>
</dl>
<ul class="icons">
<li><a href="http://github.com/jiayuzhou" target="_blank" class="icon fa-github alt"><span class="label">GitHub</span></a></li>
<li><a href="http://0xmachine.com" target="_blank" class="icon fa-home alt"><span class="label">Homepage</span></a></li>
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</section>
<p class="copyright">© 2017 Jiayu Zhou. Design: <a href="https://html5up.net">HTML5 UP</a>.</p>
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