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<!DOCTYPE html>
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<title>Syllabus - Draft</title>
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<a href="syllabus.html">Syllabus</a>
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<a href="assignments.html">Assignments</a>
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<a href="labs.html">Labs</a>
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<h1 class="title toc-ignore">Syllabus - Draft</h1>
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<hr />
<p><em>Urban planning decisions are inherently difficult, as cities comprise systems of immense complexity and increasingly large volumes of data. While planners aren’t new to qualitative and quantitative tools to model such decisions, this course will engage the role of technologies in the planning process by focusing on challenges and advantages gained from three new skills in particular: data munging, machine learning, and data visualization. Students will learn to apply the skills and techniques necessary to describe, model, and evaluate their results alongside the history and theory intersecting technocracy and urban planning.</em></p>
<div id="text-books" class="section level2">
<h2>Text Books</h2>
<p><strong>R for Everyone: Advanced Analytics and Graphics</strong> by <em>Jared Lander</em> (<a href="https://www.amazon.com/Everyone-Advanced-Analytics-Graphics-Addison-Wesley/dp/013454692X/ref=sr_1_1?ie=UTF8&qid=1518561244&sr=8-1&keywords=r+for+everyone">amazon</a>)</p>
<p><strong>R for Data Science</strong> by <em>Hadley Wickhham</em> (<a href="https://r4ds.had.co.nz/">Online</a>)</p>
<p><strong>Applied Predictive Modeling</strong> by <em>Max Kuhn and Kjell Johnson</em> (<a href="https://link-springer-com.ezproxy.cul.columbia.edu/book/10.1007%2F978-1-4614-6849-3">Online</a>)</p>
<p><strong>Text Mining with R</strong> by <em>Julia Silge and David Robinson</em> (<a href="https://www.tidytextmining.com/">Online</a>)</p>
</div>
<div id="online-resources" class="section level2">
<h2>Online Resources</h2>
<p><a href="http://onlinestatbook.com/2/index.html">Online Stat Book</a> -Basic stats knowledge</p>
</div>
<div id="assignments" class="section level2">
<h2>Assignments</h2>
<p>There will be weekly <a href="assignments.html">assignments</a>, consisting of problem sets and reading responses that reinforce and propel topics covered in class.</p>
</div>
<div id="late-assignments" class="section level2">
<h2>Late Assignments</h2>
<p>Assignments will drop a letter grade for handing in assignments 1 day late, 50% 2-7 days and assignments later than a week will not be accepted.</p>
</div>
<div id="readings" class="section level2">
<h2>Readings</h2>
<p>There will also be bi-weekly <a href="readings.html">readings</a> assigned.</p>
</div>
<div id="final-deliverable" class="section level2">
<h2>Final Deliverable</h2>
<p>The end of the class will be a <a href="https://www.kaggle.com/about/inclass/overview">Kaggle InClass</a> competition, accompanied by a report. The final report will document data/methods, including commented code, and description of analysis and conclusions.</p>
</div>
<div id="grading" class="section level2">
<h2>Grading</h2>
<ul>
<li>Assignments 40%</li>
<li>Reading Responses 20%</li>
<li>Final Deliverable 40%</li>
</ul>
</div>
<div id="gsapp-honor-system-and-plagiarism" class="section level2">
<h2>GSAPP Honor System and Plagiarism</h2>
<p>Students must adhere to the principles of <a href="https://www.arch.columbia.edu/honor-system">academic honesty</a> and ensure that all work submitted is fully theirs and adhere to the <a href="https://www.arch.columbia.edu/plagiarism-policy">GSAPP Plagarism Policy</a> set forth. Students found guilty of plagiarism or academic dishonesty will be subject to appropriate disciplinary action.</p>
</div>
<div id="topics" class="section level2">
<h2>Topics</h2>
<div id="part-i---r-basics" class="section level3">
<h3>Part I - R Basics</h3>
<ul>
<li>Getting started with R</li>
<li>R Environment - intro to RStudio</li>
<li>Essential R Programming</li>
<li>Packages</li>
<li>Data Types</li>
<li>Data Structure</li>
<li>Functions</li>
<li>Logical Operators</li>
</ul>
</div>
<div id="part-ii---working-with-data" class="section level3">
<h3>Part II - Working with Data</h3>
<ul>
<li>Data Munging</li>
<li>Simple Web Scraping</li>
<li>Reading in Data</li>
<li>Grep (regular expressions)</li>
<li>Dplyr</li>
<li>Reshape</li>
<li>Missing Data</li>
<li>Exploratory Data Analysis</li>
<li>Getting started with ggplot2</li>
<li>Histograms and Distributions</li>
<li>Rmarkdown</li>
</ul>
</div>
<div id="part-iii---machine-learning" class="section level3">
<h3>Part III - Machine Learning</h3>
<ul>
<li>What is machine learning?</li>
<li>Machine learning problems</li>
<li>Supervised vs. Unsupervised Learning</li>
<li>Regression, Classification, Clustering</li>
<li>Classification
<ul>
<li>KNN K nearest neighbors</li>
<li>KNN exercise</li>
<li>Naive Bayes</li>
<li>Decision Trees</li>
<li>Decision tree exercise</li>
</ul></li>
<li>Regression
<ul>
<li>Generalized linear models</li>
<li>GLM exercise</li>
<li>Regression with trees</li>
<li>Regression trees exercise</li>
<li>Elastic Net</li>
</ul></li>
<li>Timeseries Modeling
<ul>
<li>Autoregression</li>
<li>ARIMA</li>
<li>SARIMA</li>
</ul></li>
<li>Ensemble Models</li>
<li>Model Evaluation
<ul>
<li>Confusion matrix</li>
<li>Sensitivity, specificity, precision, recall</li>
<li>ROC curves</li>
<li>Residuals</li>
<li>P-value</li>
<li>RMSE</li>
<li>Cross Validation</li>
</ul></li>
</ul>
</div>
</div>
<div id="part-iv---final-projects" class="section level2">
<h2>Part IV - Final Projects</h2>
<ul>
<li>Inclass Kaggle + Report</li>
</ul>
<hr />
</div>
<div id="calendar" class="section level1">
<h1>Calendar</h1>
<div id="week-1---1222019" class="section level4">
<h4>Week 1 - 1/22/2019</h4>
<ul>
<li><strong>Intro Week</strong></li>
<li>Computational thinking</li>
<li>What is machine learning?</li>
<li>Machine learning applications</li>
<li><a href="labs/lab1.html">Intro to R part 1</a></li>
<li>Assignment 1 - Intro R</li>
<li>Reading (Critical Theory of Technology)</li>
<li>Reading Response 1</li>
</ul>
</div>
<div id="week-2---1292019" class="section level4">
<h4>Week 2 - 1/29/2019</h4>
<ul>
<li><strong>Intro Week</strong></li>
<li><a href="labs/lab1.html">Intro to R part 2</a></li>
<li>Assignment 2 - Functions</li>
</ul>
</div>
<div id="week-3---252019" class="section level4">
<h4>Week 3 - 2/5/2019</h4>
<ul>
<li><strong>Data Week</strong></li>
<li>APIs</li>
<li>Data Munging</li>
<li>Assignment 3 - Data Munging</li>
<li>Reading Reponse 2</li>
</ul>
</div>
<div id="week-4---2122019" class="section level4">
<h4>Week 4 - 2/12/2019</h4>
<ul>
<li><strong>Data Week</strong></li>
<li>Exploratory Data Analysis</li>
<li>Rmarkdown</li>
<li>Reading (Technological Utopianism in American Culture)</li>
<li>Assignment 4 - EDA/Reports</li>
</ul>
</div>
<div id="week-5---2192019" class="section level4">
<h4>Week 5 - 2/19/2019</h4>
<ul>
<li><strong>Regression Week</strong></li>
<li>Linear Regression</li>
<li>Assignment 5 - Mulitvariate Regression</li>
<li>Reading Reponse 3</li>
</ul>
</div>
<div id="week-6---2262019" class="section level4">
<h4>Week 6 - 2/26/2019</h4>
<ul>
<li><strong>Regression Week</strong></li>
<li>Generalized Linear Models</li>
<li>Assignment 6 - ElasticNet</li>
</ul>
</div>
<div id="week-7---352019" class="section level4">
<h4>Week 7 - 3/5/2019</h4>
<ul>
<li><strong>Forecasting Week</strong></li>
<li>Seasonality</li>
<li>Trends</li>
<li>Cycles</li>
<li>Assignment 7 - Forecasting</li>
<li>Reading Reponse 4</li>
</ul>
</div>
<div id="week-8---3122019" class="section level4">
<h4>Week 8 - 3/12/2019</h4>
<ul>
<li><strong>Classification Week</strong></li>
<li>Decision Trees</li>
<li>Reading(Rethinking Objectivity)</li>
<li>Assignment 8 - Classification</li>
</ul>
</div>
<div id="week-9---3192019-spring-break" class="section level4">
<h4>Week 9 - 3/19/2019 <strong>Spring Break</strong></h4>
</div>
<div id="week-10---3262019" class="section level4">
<h4>Week 10 - 3/26/2019</h4>
<ul>
<li><strong>Classification Week</strong></li>
<li>SVM</li>
<li>Random Forest</li>
<li>PCA</li>
<li>Reading Reponse 5</li>
<li>Assignment 9 - PCA</li>
</ul>
</div>
<div id="week-11---422019" class="section level4">
<h4>Week 11 - 4/2/2019</h4>
<ul>
<li><strong>Unsupervised Learning Week</strong></li>
<li>Kmeans</li>
<li>Heirarchical Clustering</li>
<li>Assignment 10 - Labeling</li>
</ul>
</div>
<div id="week-12---492019" class="section level4">
<h4>Week 12 - 4/9/2019</h4>
<ul>
<li><strong>Text Mining Week</strong></li>
<li>Tidy Text</li>
<li>Reading(Trust in Numbers, Weapons of Math Destruction)</li>
<li>Assignment 11 - Text Mining</li>
<li>Reading Reponse 6</li>
</ul>
</div>
<div id="week-13---4162019" class="section level4">
<h4>Week 13 - 4/16/2019</h4>
<ul>
<li><strong>Text Mining Week</strong></li>
<li>Natural Langange Processing/Understanding</li>
<li>Discussion on Final Competition</li>
<li>Assignment 12 - NLP</li>
</ul>
</div>
<div id="week-14---4232019" class="section level4">
<h4>Week 14 - 4/23/2019</h4>
<ul>
<li><strong>Model Evaluation Week</strong></li>
<li>RMSE</li>
<li>AUC</li>
<li>Confusion Matrix</li>
<li>Cross Validation</li>
<li>Reading Reponse 7</li>
</ul>
</div>
<div id="week-15---4302019" class="section level4">
<h4>Week 15 - 4/30/2019</h4>
<ul>
<li>Work day</li>
</ul>
</div>
</div>
</div>
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