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cs8850_01_intro.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
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<script src="lib/colorStringStandalone.js" charset="utf-8"></script>
<script type="text/javascript" src="lib/jquery-2.2.4.min.js"></script>
<title>Advanced Machine Learning</title>
<meta name="description" content="CS8850 GSU class">
<meta name="author" content="Sergey M Plis">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
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</head>
<body>
<div class="reveal">
<!-- In between the <div="reveal"> and the <div class="slides">-->
<!-- <header style="position: absolute; top: 10px; left: 100px; z-index: 500; font-size:100px;background-color: rgba(0,0,0,0); text-align: center !important"></header> -->
<!-- In between the <div="reveal"> and the <div class="slides">-->
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section>
<section>
<p>
<h2>Advanced Machine Learning</h2>
<h3>01: Introduction</h3>
<p>
</section>
<section>
<h3>Outline of the lecture</h3>
<ul>
<li class="fragment roll-in">Introductions
<li class="fragment roll-in">Why Machine Learning?
<li class="fragment roll-in">What is machine learning?
<li class="fragment roll-in">History of ML
<li class="fragment roll-in">Reinforcement Learning
<li class="fragment roll-in">Course Overview
<li class="fragment roll-in"> *
</ul>
</section>
</section>
<section>
<section>
<h1>Introductions</h1>
</section>
<section>
<h2>Instructor</h2>
<row>
<col70>
<h3>Sergey Plis, Ph.D.</h3>
<ul style="list-style-type: none; ">
<li>Department of Computer Science,
<li>Georgia State University
<li>55 Park Place, office 1821
<li>Office hours: TT 14:15-14:45 <em>but ask anything in slack</em> and by appointment
<li>Email: <a href="mailto:[email protected]">[email protected]</a>
</ul>
</col70>
<col30>
<img src="figures/SergeyPlis.jpg" alt="Sergey Plis" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="100%">
</col30>
</row>
</section>
<section>
<h2>Teaching Assistant</h2>
<row>
<col70>
<h3>Mansoor Ahmed</h3>
<ul style="list-style-type: none; ">
<li>Department of Computer Science,
<li>Georgia State University
<li>
<li>Office hours: by appointment
<li>Email: <a href="mailto: [email protected]">[email protected]</a>
</ul>
</col70>
<col30>
<img src="figures/MansoorAhmed.png" alt="" style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="100%">
</col30>
</row>
</section>
</section>
<section>
<section>
<h1>Why Machine Learning?</h1>
</section>
<section data-vertical-align-top data-background-iframe="https://joshworth.com/dev/pixelspace/pixelspace_solarsystem.html"
data-background-interactive>
<h3>Philosophical reason</h3>
<div class='slide-footer'>
<a href="https://joshworth.com/dev/pixelspace/pixelspace_solarsystem.html">Source</a>
</div>
</section>
<section data-vertical-align-top>
<h3>Pragmatic reason</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="700" src="figures/lesion.jpg"
alt="loop"><br>
brain imaging for brain
disorder understanding
</section>
<section>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="700" src="figures/moredata.jpg"
alt="loop"><br>
lets collect more data at the finest resolution
</section>
<section>
<h3>Problem</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="700" src="figures/overload.jpg"
alt="loop"><br>
high dimensional data is not
easy to see through
</section>
<section>
<h3>Desired Solution</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255,
0.8);" width="700" src="figures/rreading.png"
alt="loop"><br>
automatically extract
meaning from data
</section>
<section data-state="brightside" data-background="figures/robotLearning.png">
<!-- <h3>Desired Solution</h3> -->
<!-- <img class="stretch" style="border:0; box-shadow: 0px 0px 0px -->
<!-- rgba(150, 150, 255, 0.8); margin-top:-20pt;" -->
<!-- width="80%" -->
<!-- src="figures/robotLearning.png" -->
<!-- alt="loop"><br> -->
<!-- <h3> -->
<!-- automatically extract meaning from data -->
<!-- </h3> -->
<div class="slide-footer" style="font-size:9pt;">
The image is generated by Midjourney from the prompt "the robot is holding a huge book and is happily reading it looking at the pages" and a seed image
</div>
</section>
<section data-background="figures/jobnews.png"
data-background-transition="zoom"
data-background-size="contain">
</section>
<section data-background="figures/automation.jpg"
data-background-transition="zoom"
data-background-size="cover">
</section>
<section data-background="figures/job.jpg"
data-background-transition="zoom"
data-background-size="cover">
</section>
<section data-background="figures/threat.png"
data-background-transition="zoom"
data-background-size="contain">
</section>
<section data-background="figures/skynet.gif"
data-background-transition="zoom"
data-background-size="cover">
</section>
<section data-background="figures/threat.png"
data-background-transition="zoom"
data-background-size="contain">
</section>
<section data-background="figures/paperclip.jpg"
data-background-transition="zoom"
data-background-size="cover">
</section>
</section>
<section>
<section>
<h1>What is machine learning?</h1>
</section>
<section>
<h3>What is learning?</h3>
<blockquote style="text-align: left; width: 100%;" class="fragment" data-fragment-index="1">
Learning is acquiring new, or modifying
existing, knowledge, behaviors, skills, values,
or preferences and may involve synthesizing
different types of information. The ability to
learn is possessed by humans and animals.
</blockquote>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="50%" src="figures/thinking-robot_t.gif" alt="loop">
<div class='slide-footer' style="text-align: left;">
<ul>
<li><a href="http://en.wikipedia.org/wiki/Learning">http://en.wikipedia.org/wiki/Learning</a><br>
<li><a href="https://digilite.ca/guide-to-building-your-own-ai-chatbot/">gif source</a>
</ul>
</div>
</section>
<section>
<h2>Machine Learning</h2>
<blockquote style="text-align: left; width: 100%;">
Arthur Samuel (1959). Machine
Learning: Field of study that gives
computers the ability to learn without
being clearly programmed
</blockquote>
<div class='slide-footer' style="text-align: left;">
<ul>
<li><a href="https://en.wikipedia.org/wiki/Arthur_Samuel">https://en.wikipedia.org/wiki/Arthur_Samuel</a><br>
</ul>
</div>
</section>
<section>
<h2>Machine Learning</h2>
<blockquote style="text-align: left; width: 100%;" width="100%">
Tom Mitchell (1998) Well-posed
Learning Problem: A computer program
is said to learn from experience E
with respect to some task T and some
performance measure P, if its
performance on T, as measured by P,
improves with experience E.
</blockquote>
</section>
<section>
<h3>ML as a Scientific Discipline</h3>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="500"
src="figures/venn_ml.png" alt="Venn
diagramm">
</section>
<section>
<h3>ML combines</h3>
<ul>
<li class="fragment roll-in">Discrete Mathematics
<li class="fragment roll-in">Linear Algebra
<li class="fragment roll-in">Statistics
<li class="fragment roll-in">Calculus
<li class="fragment roll-in">Optimization Theory
</ul>
<p>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="50%" src="figures/bot_build.gif" alt="loop">
</section>
<section>
<h3>ML draws from</h3>
<ul>
<li class="fragment roll-in">Artificial Intelligence
<li class="fragment roll-in">Bayesian Methods
<li class="fragment roll-in">Computational Complexity
<li class="fragment roll-in">Optimization Theory
<li class="fragment roll-in">Information theory
<li class="fragment roll-in">Philosophy
<li class="fragment roll-in">Psychology and neurobiology
</ul>
<p>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="50%" src="figures/bot_build.gif" alt="loop">
</section>
<section>
<h3>Learning tasks</h3>
<ul>
<li class="fragment roll-in">Classification
<li class="fragment roll-in">Regression
<li class="fragment roll-in">Ranking
<li class="fragment roll-in">Clustering
<li class="fragment roll-in">Dimensionality reduction
<li class="fragment roll-in">Manifold learning
<li class="fragment roll-in">Causal Learning
</ul>
<p>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8); bottom: 0 !important" width="60%" src="figures/bot_dog.gif" alt="loop">
</section>
<section>
<h3>Learning Scenarios</h3>
<ul>
<li class="fragment roll-in">Supervised learning
<li class="fragment roll-in">Semi-supervised learning
<li class="fragment roll-in">Active learning
<li class="fragment roll-in">Online learning
<li class="fragment roll-in">Unsupervised learning
<li class="fragment roll-in">Reinforcement learning!
</ul>
</section>
</section>
<section>
<section data-state="brightside">
<h1>history of ML</h1>
<h2>extremely brief and biased</h2>
</section>
<section
data-state="darkside">How did we get to this point?</section>
<section>
<h2>1966</h2>
<blockquote style="width: 100%;">
Marvin Minsky (MIT, Turing Award 1969) hired a
first year undergraduate student and assigned
him a problem to solve over the summer: connect
a camera to a computer and get the machine to
describe what it sees.
</blockquote>
<div class="slide-footer">
<a href=http://www.lyndonhill.com/opinion-cvlegends.html>Urban legends in Computer Science</a>
</div>
</section>
<section data-background="figures/impossible.png"
data-background-transition="zoom"
data-state="darkside"
data-background-size="contain">
</section>
<section data-state="darkside">
<h2>ImageNet Challenge</h2>
<ul style="list-style-type: square;
list-style-position: inside;
font-size:40px;">
<!-- font-variant: small-caps; -->
<!-- font-weight: bold;"> -->
<li>14,192,122 million images, 21841 thousand categories</li>
<li>Image found via web searches for WordNet noun synsets</li>
<li>Hand verified using Mechanical Turk </li>
<li>Bounding boxes for query object labeled</li>
<li>New data for validation and testing each year</li>
</section>
<section data-background="figures/imagenet.png"
data-background-transition="zoom"
data-vertical-align-top
data-state="darkside"
data-background-size="contain">
<h2>50 years later</h2>
</section>
<section data-background="figures/perceptron.png"
data-background-transition="zoom"
data-vertical-align-top
data-state="brightside"
data-background-size="contain">
<h2>Perceptron 1958</h2>
</section>
<section data-background="figures/perceptron-pla-14-steps.gif"
data-background-transition="zoom"
data-background-size="contain">
</section>
<section data-background="figures/FF.png"
data-background-transition="zoom"
data-vertical-align-top
data-state="darkside"
data-background-size="contain">
<h3>Stacked perceptrons</h3>
</section>
<section data-background="figures/deepff.png"
data-background-transition="zoom"
data-vertical-align-top
data-state="darkside"
data-background-size="contain">
<h3>Deep Neural Net</h3>
</section>
<section data-background="figures/vgg.png"
data-background-transition="zoom"
data-vertical-align-top
data-background-size="contain">
<h3>Convolutional Neural Network</h3>
</section>
<section data-background="figures/seq2seq.png"
data-background-transition="zoom"
data-vertical-align-top
data-background-size="contain">
<h3>Recurrent Neural Network</h3>
</section>
<section>
<h3>Supervised learning</h3>
$(x_i, y_i)$
</section>
</section>
<section>
<section data-state="brightside">
<h1>Reinforcement learning</h1>
</section>
<section>
<h4>Reinforcement Learning on Atari</h4>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="500"
class="reveal" src="figures/joystik.png" alt="david">
</section>
<section>
<h3>DQN on Atari</h3>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="500"
class="reveal" src="figures/net.png" alt="david">
</section>
<section>
<h3>Results DQN on Atari (2015)</h3>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="500"
class="reveal" src="figures/histgrm.png" alt="david">
</section>
<section data-background-iframe="https://www.youtube.com/embed/V1eYniJ0Rnk?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0">
</section>
<section data-background-iframe="https://www.youtube.com/embed/v7T5El42QGE?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0">
</section>
<section data-background-iframe="https://www.youtube.com/embed/aKRve2r2C9I?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0">
</section>
</section>
<section>
<section>
<h1>Course Overview</h1>
</section>
<section>
<h2>What we'll cover</h2>
<ul>
<li class="fragment roll-in">Basics of Learning Theory
<li class="fragment roll-in">Most important (or more advanced) algorithms
<li class="fragment roll-in">Sprinkled with what the field recently learned on top of the "classical"
ML
<ul>
<li class="fragment roll-in">SGD is too powerful to ignore
<li class="fragment roll-in">Local minima are not as bad
<li class="fragment roll-in">Bias-variance trade off is an
incomplete story
<li class="fragment roll-in">Reinforcement learning is not dead
</ul>
</li>
</ul>
<div class='slide-footer'>
Subject to change
</div>
</section>
<section data-background="figures/Foundationsan_overview.png" data-background-size="contain">
</section>
<section data-background="figures/schedule_fall_2022_aml.png" data-background-size="contain">
</section>
<section>
<h2>Grade Split</h2>
<ul>
<li> 25% - Homework
<li> 25% - Midterm exam
<li> 25% - Final exam
<li> 25% - Project
</ul>
</section>
<section>
<h2>Relative Ranking</h2>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="100"
class="reveal" src="figures/competition_time.gif" alt="competition">
Send me your private nicknames ASAP if you have not done so
</section>
<section>
<h2>prerequisites</h2>
<ul>
<li class="fragment roll-in">Ability to program in your
favorite language
<li class="fragment roll-in">Ability to program in python
<li class="fragment roll-in">Basic knowledge of calculus,
linear algebra, optimization theory,
probability, and statistics
</ul>
</section>
<section>
<h2>Textbooks</h2>
"Understanding Machine Learning" Shai
Shalev-Shwartz and Shai Ben-David
<img style="border:0; box-shadow: 0px 0px 0px
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</section>
<section>
<h2>Textbooks</h2>
"Pattern Recognition and ML" C. M. Bishop
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="100%"
class="contain" src="figures/bishop_cover.png" alt="BISH">
</section>
<section>
<h2>Textbooks</h2>
"Deep Learning" I. Goodfellow,
Y. Bengio, A. Courville
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="100"
class="reveal" src="figures/dl_cover.jpg" alt="DL">
</section>
<section>
<h2>Textbooks</h2>
"Information Theory, Inference and
Learning Algorithms" David MacKay
<br>
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rgba(150, 150, 255, 0.8);" width="300"
class="reveal"
src="figures/mackay_cover.jpg"
alt="Information Theory">
</section>
<section>
<h2>Textbooks</h2>
"Data Science for Business"
<br>
<img style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="300"
class="reveal" src="figures/dsbusiness_cover.jpg" alt="DS">
</section>
</section>
<section>
<section>
<h1>The Project!</h1>
</section>
<section data-background="figures/lean_startup.png">
<h2>Lean startup</h2>
</section>
<section data-vertical-align-top data-background-iframe="https://hpmor.com/">
</section>
<section data-background="figures/ml-development-cycle.png">
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</section>
<section>
<h3>Anatomy of the project</h3>
<object id="mlproject" data="figures/project_components.svg#0" width="730"></object>
</section>
<section>
<h3>Milestones</h3>
<ul>
<li class="fragment roll-in"> Submit project ideas each (<em>deadline September 11th</em>)
<li class="fragment roll-in"> Form teams (4-6 people each team) (<em>deadline September 25th</em>)
<li class="fragment roll-in"> Present your team, your problem, and your idea how to solve it (<em>October 9th</em>)
<li class="fragment roll-in"> Submit a report (a paper NeurIPS style) (<em>deadline December 5th</em>)
<li class="fragment roll-in"> Present the project to the class (<em>either 27th or 29th of November</em>)
</ul>
</section>
<section style="width: 120%;"">
<h3>Deliverables</h3>
The project is complete if by <em>December 5th deadline</em> you submit:
<ul>
<li class="fragment roll-in"> NeurIPS style formatted report
<li class="fragment roll-in"> Link to github with the code
<li class="fragment roll-in"> Slides of the final presentation
<li class="fragment roll-in"> Bonus points:
<ul>
<li> A link to a kanban board with all tasks and people who completed them
<li> Kaggle leaderboard of a competition you were a part of for this project
<li> State of the art result possibly fit for NeurIPS (prize: help with the paper submission)
</ul>
</ul>
<div class="slide-footer" style="text-align: left;">
have you missed learning how to use your tools? check this out: <a href="https://missing.csail.mit.edu/" target="_blank">https://missing.csail.mit.edu/</a>
</div>
</section>
</section>
<section>
<h2>Pre test!</h2>
</section>
</div>
</div>
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<div id="header-right"><h4>Advanced Machine Learning</h4></div>
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