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<div id="preamble">
</div>
<div id="content">
<h1 class="title">2013-01-25-Intro</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#sec-1">1 Data Mining i290</a></li>
<li><a href="#sec-2">2 Course Goals</a></li>
<li><a href="#sec-3">3 We'll Do Stuff</a>
<ul>
<li><a href="#sec-3-1">3.1 Homework Details</a></li>
</ul>
</li>
<li><a href="#sec-4">4 But Don't Worry</a>
<ul>
<li><a href="#sec-4-1">4.1 Help</a></li>
</ul>
</li>
<li><a href="#sec-5">5 This is a Graduate class</a>
<ul>
<li><a href="#sec-5-1">5.1 Style</a></li>
</ul>
</li>
<li><a href="#sec-6">6 Prerequisites</a>
<ul>
<li><a href="#sec-6-1">6.1 Basics</a></li>
</ul>
</li>
<li><a href="#sec-7">7 Material</a>
<ul>
<li><a href="#sec-7-1">7.1 What will we learn?</a></li>
</ul>
</li>
<li><a href="#sec-8">8 Lectures & Labs</a>
<ul>
<li><a href="#sec-8-1">8.1 Helpful tips</a></li>
</ul>
</li>
<li><a href="#sec-9">9 Office Hours</a>
<ul>
<li><a href="#sec-9-1">9.1 Details</a></li>
</ul>
</li>
<li><a href="#sec-10">10 <b>Questions?</b></a></li>
<li><a href="#sec-11">11 Schedule</a></li>
<li><a href="#sec-12">12 Hi, I'm Jim Blomo</a></li>
<li><a href="#sec-13">13 Hi, I'm Shreyas</a></li>
<li><a href="#sec-14">14 Data is Important</a>
<ul>
<li><a href="#sec-14-1">14.1 Decisions</a></li>
</ul>
</li>
<li><a href="#sec-15">15 Data is Important</a>
<ul>
<li><a href="#sec-15-1">15.1 Nice example of data mining</a></li>
</ul>
</li>
<li><a href="#sec-16">16 Data Mining ecosystem</a>
<ul>
<li><a href="#sec-16-1">16.1 Ecosystem</a></li>
<li><a href="#sec-16-2">16.2 Analysis vs. Data Mining</a>
<ul>
<li><a href="#sec-16-2-1">16.2.1 Pedantic</a></li>
</ul>
</li>
<li><a href="#sec-16-3">16.3 Machine Learning</a>
<ul>
<li><a href="#sec-16-3-1">16.3.1 Uses</a></li>
</ul>
</li>
<li><a href="#sec-16-4">16.4 Probability & Statistics</a>
<ul>
<li><a href="#sec-16-4-1">16.4.1 Uses</a></li>
</ul></li>
</ul>
</li>
<li><a href="#sec-17">17 Process</a>
<ul>
<li><a href="#sec-17-1">17.1 Common Themes</a></li>
</ul>
</li>
<li><a href="#sec-18">18 <b>Break</b></a></li>
</ul>
</div>
</div>
<div id="outline-container-1" class="outline-2">
<h2 id="sec-1"><span class="section-number-2">1</span> Data Mining i290 <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-1">
<ul>
<li>Jim Blomo & Shreyas
</li>
</ul>
</div>
</div>
<div id="outline-container-2" class="outline-2">
<h2 id="sec-2"><span class="section-number-2">2</span> Course Goals <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-2">
<ul>
<li>Extract <b>information</b> from <b>data</b>
</li>
<li>Understand techniques to find patterns
</li>
<li>Apply algorithms to real data sets
</li>
</ul>
</div>
</div>
<div id="outline-container-3" class="outline-2">
<h2 id="sec-3"><span class="section-number-2">3</span> We'll Do Stuff <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-3">
<ul>
<li>30%: 10 Homework Assignments
</li>
<li>30%: 1 Midterm
</li>
<li>40%: 1 Project: Find, Mine, Report on Data
</li>
</ul>
</div>
<div id="outline-container-3-1" class="outline-3">
<h3 id="sec-3-1"><span class="section-number-3">3.1</span> Homework Details <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-3-1">
<ul>
<li>HW due at midnight Thursday before class
</li>
<li>Each 24 hours late is 10% off
</li>
<li>HW will be turned in by GitHub pull request
</li>
<li>Project will be submitted by email & presentation
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-4" class="outline-2">
<h2 id="sec-4"><span class="section-number-2">4</span> But Don't Worry <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-4">
<ul>
<li>This isn't a programming class
</li>
<li>Grades are based on understanding of the concepts, not the craziest project
</li>
<li>Shreyas & I are here to help
</li>
</ul>
</div>
<div id="outline-container-4-1" class="outline-3">
<h3 id="sec-4-1"><span class="section-number-3">4.1</span> Help <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-4-1">
<ul>
<li>We realize there's a wide range of technical skill
</li>
<li>We will help get anyone up to speed in these technical areas
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-5" class="outline-2">
<h2 id="sec-5"><span class="section-number-2">5</span> This is a Graduate class <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-5">
<ul>
<li>Perform well without supervision
</li>
<li>Readings from both book and online documentation
</li>
<li>TMTOWTDI
</li>
<li>Getting frameworks working on your computer
</li>
</ul>
</div>
<div id="outline-container-5-1" class="outline-3">
<h3 id="sec-5-1"><span class="section-number-3">5.1</span> Style <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-5-1">
<ul>
<li>More firehouse than spoon feed, you'll need to follow up for
understanding
</li>
<li>Honor system: No copying code or answers. Helping each other with
concepts is encouraged, but document it.
</li>
<li>Everybody has a different workflow. We'll be covering the most basic.
Great if you want to do something different, but realize we may not be able
to help you as much.
</li>
<li>Non ISchool students should email student ID from EDU account to shreyas and
jblomo and we will get them ischool accounts.
</li>
<li>You may want to use other frameworks for your projects. Great! But again,
we may not be familiar with them
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-6" class="outline-2">
<h2 id="sec-6"><span class="section-number-2">6</span> Prerequisites <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-6">
<ul>
<li>Basic probability: P(A), P(A or B), P(A and B), P(A | B)
</li>
<li>Basic programming: Python
</li>
<li>Basic command line: SSH, downloading, copying large files, running programs
against data
</li>
<li>Textbook: Han, J., Kamber, M., & Pei, J. (2011). <span style="text-decoration:underline;">Data Mining: Concepts and Techniques</span>, Third Edition <b>(3rd ed.)</b>. Morgan Kaufmann.
</li>
<li>Technology will be available on <code>ischool.berkeley.edu</code>
</li>
</ul>
</div>
<div id="outline-container-6-1" class="outline-3">
<h3 id="sec-6-1"><span class="section-number-3">6.1</span> Basics <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-6-1">
<ul>
<li>"Probability of A", "Probability of A or B" "A and B" "A given B"
</li>
<li>Most assignments filling in algorithm code
</li>
<li>Project you may use any language, though we suggest Python.
</li>
<li>We'll introduce any specific frameworks
</li>
<li>Command line: cp, mv, less… Imagine you have a 10G file, how are you
going to inspect the contents?
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-7" class="outline-2">
<h2 id="sec-7"><span class="section-number-2">7</span> Material <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-7">
<ul>
<li>Process: from find data to mining it to visualizing results
</li>
<li>Algorithms: all intuitively motivated, some rigorously studied
</li>
<li>Programming: using algorithms against data sets
</li>
<li>Discovery: finding information in self-defined project
</li>
</ul>
</div>
<div id="outline-container-7-1" class="outline-3">
<h3 id="sec-7-1"><span class="section-number-3">7.1</span> What will we learn? <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-7-1">
<ul>
<li>Data mining not just about algorithms. We'll learn how to obtain, clean,
and store data.
</li>
<li>In real life, this is 70% of the job!
</li>
<li>We'll cover many different algorithms, and dive in depth on several of
them. But we're not going to get into any hairy math proofs
</li>
<li>Programming is the best way to precisely describe an algorithm. It is also
the way data mining is used in the real world.
</li>
<li>Your own project should emphasize your passion. Again, real world requires
you to grab data and squeeze information out of it without external help
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-8" class="outline-2">
<h2 id="sec-8"><span class="section-number-2">8</span> Lectures & Labs <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-8">
<ul>
<li>Start with Q&A for at least 10 minutes
</li>
<li>Expect to be asked a question
</li>
<li>Breaks
</li>
<li>Lab: Stick around and get the first question of HW done
</li>
<li>Slides on <a href="http://jblomo.github.com/datamining290/">http://jblomo.github.com/datamining290/</a>
</li>
</ul>
</div>
<div id="outline-container-8-1" class="outline-3">
<h3 id="sec-8-1"><span class="section-number-3">8.1</span> Helpful tips <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-8-1">
<ul>
<li>Helpful to me if you say your name
</li>
<li>Sorry, I tend to forget names
</li>
<li>If I am not calling on you, check to make sure you are on the class list!
</li>
<li>I'm not taking attendance, but let me know if you can't make it so I
won't call on you
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-9" class="outline-2">
<h2 id="sec-9"><span class="section-number-2">9</span> Office Hours <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-9">
<ul>
<li>We'll stay after class
</li>
<li>or schedule a Skype call
</li>
<li><a href="https://piazza.com/class#spring2013/i290">Piazza</a> for questions and
announcements
</li>
<li>Wait list will be processed normally until 3rd week… then I'll accept
everyone who's participated in class if we have physical room
</li>
</ul>
<p> <img src="img/Office_Hours.png" alt="img/Office_Hours.png" />
</p>
</div>
<div id="outline-container-9-1" class="outline-3">
<h3 id="sec-9-1"><span class="section-number-3">9.1</span> Details <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-9-1">
<ul>
<li>I expect that everyone will be able to get into the class
</li>
<li>img src: <a href="http://statweb.calpoly.edu/srein/">http://statweb.calpoly.edu/srein/</a>
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-10" class="outline-2">
<h2 id="sec-10"><span class="section-number-2">10</span> <b>Questions?</b> <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-10">
</div>
</div>
<div id="outline-container-11" class="outline-2">
<h2 id="sec-11"><span class="section-number-2">11</span> Schedule <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-11">
<p>Available at <a href="http://jblomo.github.com/datamining290/">GitHub Syllabus page</a>
</p><ul>
<li>Jan 25 Class Intro ; Tools Intro by <i>GUEST: Shreyas</i>
<ul>
<li>lab: Git Intro
</li>
</ul>
</li>
<li>Feb 1 Case Studies ; Obtaining Data
</li>
<li>Feb 8 Probability ; Preprocessing
</li>
<li>Feb 15 MapReduce, Data Warehouse
</li>
<li>Feb 22 Decision Trees; Naive Bayes
</li>
<li>Mar 1 SVM ; Neural Networks
</li>
<li>Mar 8 Clustering ; Review
<ul>
<li>lab: Project Proposal Due
</li>
</ul>
</li>
<li>Mar 15 <b>Midterm</b>
<ul>
<li>lab: -
</li>
</ul>
</li>
<li>Mar 21 Dimensionality Curse ; Graph Mining
</li>
<li>Mar 29 HOLIDAY
</li>
<li>Apr 5 Pattern ; Evaluations
</li>
<li>Apr 12 Collaborative Filtering; PageRank
</li>
<li>Apr 19 Feature Extraction ; Evaluation
</li>
<li>Apr 26 Images ; Audio
</li>
<li>May 3 Visualization ; HTML
</li>
<li>May 10 In Real Life ; Review
<ul>
<li>lab: -
</li>
</ul>
</li>
<li>May 17 Final Presentation
<ul>
<li>lab: Bye!
</li>
</ul>
</li>
</ul>
</div>
</div>
<div id="outline-container-12" class="outline-2">
<h2 id="sec-12"><span class="section-number-2">12</span> Hi, I'm Jim Blomo <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-12">
<p>*<a href="https://www.dropbox.com/s/obnsldacg355wqn/2013-01-08 21.50.03.mp4">Hello Class!</a>*
</p>
<ul>
<li>Cal EECS
</li>
<li>A9 - Amazon Search
</li>
<li>PBworks
</li>
<li>Yelp
</li>
<li>Lecturer
</li>
</ul>
</div>
</div>
<div id="outline-container-13" class="outline-2">
<h2 id="sec-13"><span class="section-number-2">13</span> Hi, I'm Shreyas <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-13">
<ul>
<li>First year Grad Student (MIMS '14)
</li>
<li>Also TA'd Analyzing Big Data class
</li>
<li>I can be reached at <code>[email protected]</code>
</li>
</ul>
</div>
</div>
<div id="outline-container-14" class="outline-2">
<h2 id="sec-14"><span class="section-number-2">14</span> Data is Important <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-14">
<ul>
<li>Making decisions is a core part of humanity
</li>
<li>Data can help you make better decisions
</li>
<li>Challenge: extract information from data to improve decisions
</li>
</ul>
</div>
<div id="outline-container-14-1" class="outline-3">
<h3 id="sec-14-1"><span class="section-number-3">14.1</span> Decisions <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-14-1">
<ul>
<li>From big to small; from planning to execution
</li>
<li>Business questions: what is the ROI of this feature? Where to concentrate
development?
</li>
<li>Personal questions: Where to eat dinner tonight? What movie to see?
</li>
<li>Improving decisions means improving quality of life
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-15" class="outline-2">
<h2 id="sec-15"><span class="section-number-2">15</span> Data is Important <span class="tag"><span class="slide">slide</span> <span class="center">center</span></span></h2>
<div class="outline-text-2" id="text-15">
<iframe width="560" height="315" src="http://www.youtube.com/embed/y7een27u1GM" frameborder="0" allowfullscreen></iframe>
</div>
<div id="outline-container-15-1" class="outline-3">
<h3 id="sec-15-1"><span class="section-number-3">15.1</span> Nice example of data mining <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-15-1">
<ul>
<li>Stop at 3:51
</li>
<li>Had to work with external parties to get data (Yelp, city of Seattle)
</li>
<li>Had to clean data (literally, sometimes he was just handed paper receipts)
</li>
<li>Used regression analysis to discover patterns
</li>
<li>created follow up questions
</li>
<li>Used result to understand the meaning behind the data
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-16" class="outline-2">
<h2 id="sec-16"><span class="section-number-2">16</span> Data Mining ecosystem <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-16">
<ul>
<li>Data mining is part of a process to make decisions from data
</li>
<li>Intersection between statistics, computer science, data management, machine
learning
</li>
<li>Analysis & visualization often required
</li>
</ul>
</div>
<div id="outline-container-16-1" class="outline-3">
<h3 id="sec-16-1"><span class="section-number-3">16.1</span> Ecosystem <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-16-1">
<ul>
<li>We'll talk about several ways to think about the process from data to
knowledge
</li>
<li>No universally agreed process, or black-and-white boundaries
</li>
<li>Analysis: used at the beginning of investigations to understand data
characteristics
</li>
<li>Visualization: better understanding of the results of analysis or data
mining
</li>
</ul>
</div>
</div>
<div id="outline-container-16-2" class="outline-3">
<h3 id="sec-16-2"><span class="section-number-3">16.2</span> Analysis vs. Data Mining <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-2">
<ul>
<li><b>Analysis</b>: manually investigating data. No algorithms.
</li>
<li>Statistical qualities: mean, median, standard deviation
</li>
<li>Histograms (manually set buckets)
</li>
<li>Counts / Percentages
</li>
</ul>
<ul>
<li><b>Data Mining</b>: discovering patterns though automated algorithms
</li>
<li>Regressions: fitting data to a model
</li>
<li>Clustering: grouping data without manually set descriptions
</li>
<li>Classification: identifying divisive features
</li>
</ul>
</div>
<div id="outline-container-16-2-1" class="outline-4">
<h4 id="sec-16-2-1"><span class="section-number-4">16.2.1</span> Pedantic <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-2-1">
<ul>
<li>Difference is subtle, but important for both the project and your resume
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-16-3" class="outline-3">
<h3 id="sec-16-3"><span class="section-number-3">16.3</span> Machine Learning <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-3">
<ul>
<li>Programs that can learn from data
</li>
<li>Focus on prediction, based on verified training data
</li>
<li>Used in two ways: during DM, after DM
</li>
</ul>
<p> <img src="img/Terminator.jpg" alt="img/Terminator.jpg" />
</p>
</div>
<div id="outline-container-16-3-1" class="outline-4">
<h4 id="sec-16-3-1"><span class="section-number-4">16.3.1</span> Uses <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-3-1">
<dl>
<dt>During</dt><dd>assume we have training data, train on it, see how useful trained
program is or find outliers
</dd>
<dt>After</dt><dd>Discover clusters, verify and label clusters. Use labelled clusters
to train a program to recognize new data points
</dd>
</dl>
</div>
</div>
</div>
<div id="outline-container-16-4" class="outline-3">
<h3 id="sec-16-4"><span class="section-number-3">16.4</span> Probability & Statistics <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h3>
<div class="outline-text-3" id="text-16-4">
<p> <img src="img/Poisson_cdf.svg.png" alt="img/Poisson_cdf.svg.png" />
</p><ul>
<li>Data describes real world events
</li>
<li>Probability can describe real world <b>expected</b> events
</li>
<li>Distributions can be used to summarize data, understand the factors behind
its creation
</li>
</ul>
</div>
<div id="outline-container-16-4-1" class="outline-4">
<h4 id="sec-16-4-1"><span class="section-number-4">16.4.1</span> Uses <span class="tag"><span class="notes">notes</span></span></h4>
<div class="outline-text-4" id="text-16-4-1">
<ul>
<li>Can "fit" data to a distribution, find outliers that are unexpected
</li>
<li>An example: Poisson distribution describes the expectation of a particular
number of events occurring.
<ul>
<li>Eg. pieces of mail. average is 4, but it can vary. Is getting 7 or more
pieces of mail really an outlier?
</li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</div>
<div id="outline-container-17" class="outline-2">
<h2 id="sec-17"><span class="section-number-2">17</span> Process <span class="tag"><span class="slide">slide</span> <span class="two_col">two_col</span></span></h2>
<div class="outline-text-2" id="text-17">
<ul>
<li><b>Knowledge Discovery in Databases (KDD)</b>
</li>
<li>Selection
</li>
<li>Pre-processing
</li>
<li>Transformation
</li>
<li>Data Mining
</li>
<li>Interpretation/Evaluation
</li>
</ul>
<ul>
<li><b>Cross Industry Standard Process for Data Mining</b>
</li>
<li>Business Understanding
</li>
<li>Data Understanding
</li>
<li>Data Preparation
</li>
<li>Modeling
</li>
<li>Evaluation
</li>
<li>Deployment
</li>
</ul>
</div>
<div id="outline-container-17-1" class="outline-3">
<h3 id="sec-17-1"><span class="section-number-3">17.1</span> Common Themes <span class="tag"><span class="notes">notes</span></span></h3>
<div class="outline-text-3" id="text-17-1">
<ul>
<li>Figure out what you want to do
</li>
<li>Get the data
</li>
<li>Make sure it's OK
</li>
<li>Understanding
</li>
<li>Make a decision, test its effectiveness
</li>
<li>Reading will cover another process, aimed at "Data Science", but basically
applies to Data Mining
</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-18" class="outline-2">
<h2 id="sec-18"><span class="section-number-2">18</span> <b>Break</b> <span class="tag"><span class="slide">slide</span></span></h2>
<div class="outline-text-2" id="text-18">
<script type="text/javascript" src="production/org-html-slideshow.js"></script>
</div>
</div>
</div>
<div id="postamble">
<p class="date">Date: 2013-02-01 11:00:01 PST</p>
<p class="author">Author: Jim Blomo</p>
<p class="creator">Org version 7.8.02 with Emacs version 23</p>
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