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<html lang="en">
<head>
<style>
p {
text-align: justify; }
ul li {
list-style-position: inside; }
</style>
<link rel="icon" type="image/x-icon" href="img/logo.ico">
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<meta name="description" content="Introduction to Data Science" />
<meta name="author" content="John P. Dickerson" />
<link rel="icon" href="favicon.ico" />
<title>Introduction to Data Science</title>
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<![endif]-->
<!-- Fancy fonts for the headers -->
<link href="https://fonts.googleapis.com/css?family=Baloo+Paaji" rel="stylesheet" />
</head>
<body>
<div class="container">
<!-- Header and Navigation -->
<div class="header clearfix">
<nav>
<ul class="nav nav-pills pull-right">
<li role="presentation"><a href="#description">Description</a></li>
<li role="presentation"><a href="#schedule">Schedule</a></li>
<li role="presentation"><a href="#assignments">Deliverables</a></li>
</ul>
</nav>
<h3 class="text-muted">CMSC/DATA 320 – Summer 2026 (Term I) </h3>
</div>
<!-- Square with Basic Info -->
<div class="jumbotron">
<h2>Introduction to Data Science</h2>
<!-- <img class="img-responsive" src="img/ds_01.jpg" alt="Data Science!?"> <!-- image from Vanguard -->
<!-- <img class="img-responsive" src="img/ds_02.jpg" alt="Data Science!?"> <!-- image from Forbes -->
<!-- <img class="img-responsive" src="img/ds_03.jpg" alt="Data Science!?"> <!-- image from Business Intelligence -->
<!-- <img class="img-responsive" src="img/ds_04.png" alt="Data Science!?"> <!-- image from https://gcn.com/articles/2017/12/05/data-inventory.aspx -->
<!-- <img class="img-responsive" src="img/ds_05.jpeg" alt="Data Science!?" /> <!-- image from Amber; not sure of the source-->
<!-- <img class="img-responsive" src="img/ds_06.png" alt="Data Science!?" /> <!-- image from Wikipedia (Sociology) -->
<img class="img-responsive" src="img/OIG.jpeg" alt="Header image to data science course website showing a multi-colored cube or matrix" height="100"/> <!-- image from https://hbr.org/2018/10/prioritize-which-data-skills-your-company-needs-with-this-2x2-matrix -->
<p>
<address>
<!-- Aishani Edit Start -->
<strong>Instructor: </strong>
<div class="instructor">
<div class="ta-group">
<img src="img/headshots/dr_fardina.JPG" alt="Dr. Fardina" width="70" height="70">
<a href="https://www.fardinafathmiulalam.com/">Dr. Fardina Alam</a> (fardina@umd.edu)
</div>
</div>
<!-- <strong>Head Teaching Assistants (TAs): </strong>
<div class="head-tas">
<div class="ta-group">
<img src="img/headshots/aviva.JPG" alt="Aviva" width="70" height="70">
<a href="">Aviva Prins</a>
</div>
<div class="ta-group">
<img src="img/headshots/aishani.JPG" alt="Aishani" width="70" height="70">
<a href="">Aishani Mukherjee</a>
</div>
<div class="ta-group">
<img src="img/headshots/pulkit.JPG" alt="Pulkit" width="70" height="70">
<a href="">Pulkit Kumar</a>
</div>
</div> -->
<strong>Teaching Assistants (TAs): </strong>
<div class="tas-section" style="display:flex; gap:20px;">
<div class="ta-group">
<!-- <img src="img/headshots/aishanim.JPG" alt="Aishani" width="70" height="70">
<a href="">Aishani Mukherjee</a>
</div>
<div class="ta-group">
<img src="img/headshots/pulkit.JPG" alt="Pulkit" width="70" height="70">
<a href="">Pulkit Kumar</a>
</div> -->
<div class="ta-group">
<img src="img/headshots/guanhong.JPG" alt="Guanhong" width="70" height="70">
<a href="">Guanhong Wang</a>
</div>
<div class="ta-group">
<img src="img/headshots/shengjie.JPG" alt="Shengjie" width="70" height="70">
<a href="">Shengjie Xu</a>
</div>
</div>
<div class="lecture-schedule">
<strong>Lecture Schedule:</strong>
ONLINE (Combination of Synchronous and Asynchronous Sessions): 11:30 AM - 12:50 PM EDT, Tuesday to Friday.
</div>
<br/>
<br/>
The Zoom links for the lectures will be posted on ELMS. Class recordings for both sessions will be posted on Panopto via ELMS.
</address>
<style>
address {
text-align: center;
}
.instructor, .head-tas, .tas-section {
display: flex;
justify-content: center;
gap: 30px;
flex-wrap: wrap;
margin: 15px 0;
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margin: 10px;
min-width: 100px;
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margin-bottom: 8px;
}
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margin-top: 20px;
}
a {
display: block;
margin: 2px 0;
}
</style>
</p>
</div>
<!-- Aishani Edit End -->
<hr class="featurette-divider">
<!-- Big Class Description -->
<div class="row marketing" id="description">
<h1>Course Overview</h1>
<p>Data science encapsulates the interdisciplinary activities required to create data-centric products and applications that address specific scientific, socio-political, or business questions. It has drawn tremendous attention from both academia and industry and is making deep inroads in industry, government, healthcare, and journalism.</p>
<p>This course focuses on (i) data management systems, (ii) exploratory and statistical data analysis, (iii) data and information visualization, and (iv) the presentation and communication of analysis results. The course will be centered around case studies drawing extensively from real-world applications and will culminate in a publicly available final project or tutorial that strengthens students’ data science portfolios.</p>
<p>The course will primarily consist of self-contained lectures and assignments that leverage real-world data science platforms when appropriate. There is an assigned textbook for this course, and the instructor will provide textbook information, reading materials, and additional resources via this Course Website, ELMS and Piazza throughout the semester.</p>
<p><strong><span style="color: red;">Note:</span></strong> <strong>This course outline is tentative and subject to modification to meet the specific needs and requirements of the students and the evolving field of data science.</strong></p>
<h3>Prerequisites</h3>
<p>
Students enrolled in the course should be comfortable with programming (for those at UMD, having passed CMSC216 will be good enough!) and be reasonably mathematically mature. The course will heavily utilize the <a href="https://www.python.org/">Python</a> scripting language through <a href="https://colab.google/">Google Colab</a> (or sometimes <a href="https://jupyter.org/">Jupyter</a> Notebook if needed), relying on the <a href="http://conda.pydata.org/docs/">Anaconda</a> package manager. Primer lectures on Python for data science will be provided early on, so there's no need to worry if you haven't used Python before. Later lectures will delve into statistics and machine learning, potentially incorporating basic calculus and basic linear algebra. A light mathematical maturity, roughly at the level of a junior CS student, is preferred.
</p>
<p><em>This course is aimed at junior- and senior-level Computer Science majors, but should be accessible to any student of life with some degree of mathematical and statistical maturity, reasonable experience with programming, and an interest in the topic area. If in doubt, e-mail me: fardina@umd.edu!</em></p>
<h3> Tentative Topics:</h3>
<p> Introduction to Data Science, Experiment Design, Introduction to Python, Data Types, Data collection, Git, Pandas, Database, SQL, Probability, Summary Statistics, Hypothesis Testing, Data Visualization, Data Exploration, Introduction to Machine Learning, Classifications, Decision Trees, Regression, Feature Engineering, ML Evaluation, Neural Network, Image Processing, Natural Language Processing (NLP)
Introduction to Graph(s) Theory, Recommender Systems, Data Science Ethics
</p>
<h3> Learning Outcomes: </h3>
<p> At the completion of this course, students will be able to:
<ul>
<li>Develop a foundational understanding of data science, encompassing key concepts, methodologies, and tools.</li>
<li>Proficiently use Python for data science applications, including data types, collection, and manipulation using libraries like Pandas.</li>
<li>Explore machine learning concepts, including classifications, decision trees, regression, and feature engineering.</li>
<li>Evaluate machine learning models using appropriate metrics, ensuring a comprehensive understanding of model performance.</li>
<li>Understand advanced topics such as neural networks, image processing, natural language processing (NLP) etc.</li>
<li>Understand the importance of applying ethical frameworks and guidelines to make informed decisions in real-world data scenarios.</li>
</ul>
By the end of the course, students will have acquired a versatile skill set, enabling them to tackle real-world data science challenges with confidence and ethical awareness.
</p>
<h3>Grading</h3>
<p>There will be four assignments, one semester long final group project or tutorial, weekly online quizzes, and one final written exams (cumulative).</p>
<p> Final grades will be calculated as:<br>
<table class="table table-striped">
<tr>
<th>Component</th>
<th>Percentages</th>
</tr>
<tr>
<td>Assignments/Mini Projects</td>
<td>40%</td>
</tr>
<tr>
<td>Quizzes</td>
<td>20%</td>
</tr>
<tr>
<td>Semester Long Final Group Project/Tutorial</td>
<td>25%</td>
</tr>
<tr>
<td>Final Exam</td>
<td>15%</td>
</tr>
</table>
</p>
<h4>Late Policy</h4>
<p>Late work gets no credit.</p>
<p> It is recommended to submit homework and projects on time. There will be a <strong>15% penalty for late submissions of homework</strong> and a <strong>20% penalty for late submissions of project/tutorial checkpoints 1 and 2</strong> within 24 hours after the deadline</strong>.
<strong>After this 24-hour period, no submissions will be accepted</strong>. In <strong>ELMS/Gradescope (as instructed in the assignment or project)</strong>,
you can submit multiple times, and only the last submission will be graded. The penalty for late homework will be applied automatically; no request is necessary.
This policy applies to both homework and projects, EXCEPT for the <strong>final submission/checkpoint 3 of the final project/tutorial and for any kind of BONUS work</strong>
</p>
<p>See the next section about how to contact us in special circumstances. We aim to help everyone
succeed.
</p>
<h3>Communication & Logistics Request</h3>
<p>We are going to use a combination of in-person and online office hours and the Piazza forum (<a href="https://piazza.com/umd/summer2026/cmsc320">sign up here</a>) for Q&A. This means that it's appropriate to use Piazza for asynchronous communication with the course instructors and other students, and also for short, high-bandwidth discussions that could usually take place before/after class.</p>
<p>Note that Piazza is not appropriate for things like asking for accommodations, extensions or other such issues/concerns.
For any logistics-related help, such as extensions or grading issues, please <strong>fill out this form</strong>: CMSC320 Logistics Request Form.
<a href="https://cmsc320.github.io/Request_form">CMSC320- Logistic Request Form</a>
However, If you do not receive a response or if your issue is not resolved within 48 hours, then please email: </p>
<ul>
<li><a href="mailto:cmsc320gradingissues.dr.fardina@gmail.com">cmsc320gradingissues.dr.fardina@gmail.com</a> for any grading, logistics, and extension issues</li>
<!-- <li><a href="mailto:cmsc320logistics.dr.fardina@gmail.com">cmsc320logistics.dr.fardina@gmail.com</a> for logistics and extensions</li> -->
</ul>
<p> While sending an email, at the beginning of your email subject line, include relevant tags, such as <code>[hw_extension/req]</code>, <code>[project_extension/req]</code>,<code> [gradeissue]</code>,
etc. More tags will be updated on the website. <br>
If you have a request that fits into one of these categories and you <em>don't</em> email the address given above,
then your request may not reach the right person and may not be answered in a timely manner.</p>
<p>If your correspondence does not fit into either of those two categories, please email an instructor ( professor) <strong>with
<code><span style="color: red;">[CMSC320]</span></code> in the email subject line.</strong> You may also go to an instructor's office hours at the times listed below.
</p>
<p><strong>Please note: if you don't include <code>[CMSC320]</code> in your subject line when emailing instructors, your email
may not be filtered correctly.</strong></p>
<h3>Office Hours</h3>
<p> <em>* All hours are EDT</em></p>
<h4>Instructor Office Hours and TA office hours details can be found on ELMS as well as on Piazza.
</h4>
<!-- <!-- <h4>Instructor Office Hours</h4>
<p>
<!-- Instructor Office Hour Table-->
<!-- <table class="table table-striped">
<thead>
<tr>
<th style="width: 30%">Instructor</th>
<th style="width: 45%">Time</th>
<th style="width: 25%">Location</th>
</tr>
</thead>
<tbody>
<tr>
<td>Dr. Fardina Alam</td>
<td>Time and Day: TBD</td>
<td>IRB 2222</td>
</tr>
</tbody>
</table>
</p> -->
<!-- <!-- <h4>TA Office Hours</h4> -->
<!-- <p>You can find TA office hours locations for all your courses
<a href="http://www.cs.umd.edu/class/resources/cstarooms/fallspring/">here</a>.
CMSC320's assigned space is <strong>AVW 4122</strong>, and
if TAs are hosting hours virtually, you can find the zoom link next to their names.</p> -->
<p><strong>
Do keep an eye on Piazza, though; TAs will sometimes swap hours, shift hours
on Zoom, and so on!</strong> Additionally, we have at least one TA
explicitly covering Piazza on most weekdays; all course staff will float around Piazza in general, too!</p>
<hr class="featurette-divider">
<!-- Big Schedule Table -->
<div class="row marketing" id="schedule">
<h1>Schedule</h1>
<p><strong>ALL THE LECTURE SLIDES WILL BE POSTED HERE</strong></p>
<p>
The schedule is subject to change as the semester progresses!
</p>
<table class="table table-striped" style="width:100%">
<caption></caption>
<thead>
<tr>
<th>#</th>
<th >Date</th>
<th style="width:70%">Topic</th>
<th>Sync. (S) / Async. (A)</th>
<th>Reading</th>
<th>Slides</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<th scope="row">1</th>
<td>Jun 01</td>
<td> Introduction to Data Science (1.1) and Data Types (1.2)
</td>
<td>
S: 1.1 </br>
A: 1.2
</td>
<td>
Self-Study Slide: </br>
<a href="https://docs.google.com/presentation/d/1tMAcTbH0yPA4fVasPqndfbQdkSWuWLSX/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Git </a> </br>
Additional Reading/Helpful slide: </br>
<a href="https://docs.google.com/presentation/d/1tOC0EOiF4Dkavjz3To07wXC_EdKW32qvZyN3XizvMlo/edit?usp=sharing">Python</a>
<a href="https://colab.research.google.com/drive/1xHJHDJPEAZCrUeB1-iIFm8a3Rw3od5In?usp=sharing">Demo Python</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1ZyE9uPeSj2ho7dPrdr-zYgoKSwaWNdM1/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec1.1Intro</a>
<a href="https://docs.google.com/presentation/d/1zQXRWRILAOb3uQ2z-9gL4W0n90c2PcVd/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec1.2DataTypes</a>
</td>
<td>Sign up on <a href="https://piazza.com/umd/summer2024/cmsc320">Piazza</a>! </br>
HW1 Out by Jun 01) </br>
Final Project/Tutorial Instruction Out (by Jun 01)
</td>
</tr>
<tr>
<th scope="row">2</th>
<td>Jun 02</td>
<td>Experiment Design
</td>
<td>
S
</td>
<td>
<a href="http://www.stat.yale.edu/Courses/1997-98/101/expdes.htm">Experimentation </a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1smnKogiZsOvs0sCg0cd9qNOoYtBMEHMF/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec02ExpDesign</a>
</td>
<td></td>
</tr>
<tr>
<th scope="row">3</th>
<td>Jun 03</td>
<td>Panda, Database and SQL
</td>
<td>
A
</td>
<td>
<!-- <a href="https://www.kaggle.com/fardinafalam/cmsc-320-pandas-python-library-lecture03-fall2023">Pandas Demo (Fardina)</a> -->
</td>
<td>
<a href="https://docs.google.com/presentation/d/1xPRSfZh3rhIrNstFMO-MObla3VMZnwwg/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec03Part1_Pandas</a>
<a href="https://docs.google.com/presentation/d/1hOdDrVNMqiQRBICMN4Qj8C-6CDU08cKp/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec03Part2_SQL</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">4</th>
<td>Jun 04</td>
<td>Hypothesis Testing
</td>
<td>
S
</td>
<td>
<!-- <a href="https://vitalflux.com/data-science-how-to-formulate-hypothesis-for-hypothesis-testing/">Hypo Testing Steps And Examples</a>
<a href="https://towardsdatascience.com/what-is-p-value-370056b8244d">P-Value-Explaination</a>
-->
</td>
<td>
<a href="https://docs.google.com/presentation/d/1hXmp-kMZoccID6CF1WheQqnnOOUSKcYr/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec04Hypothesis_Part01</a> <br>
<a href="https://docs.google.com/presentation/d/1lUM7tmTw5UGRLynt843X9mdOL23E9PQc/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec04Hypothesis_Part02</a> <br>
<a href="https://docs.google.com/presentation/d/1LoYbhH2WT3IeedHQZJKaLxTf02erfb4D/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec04Hypothesis_Part03</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">5</th>
<td>Jun 05</td>
<td> Probability, Distributions, and Summary Stats
</td>
<td>
S (30 minutes to complete HT lecture), A (Probability and Summary Statistics Part)
</td>
<td>
<a href="https://drive.google.com/file/d/172DG9tr1rARfNfB4qYN06nTjVnl2GFyJ/view?usp=sharing"> 6. Probability (Bayes thm,Law of tot prob)</a>
<a href="https://towardsdatascience.com/clearly-explained-the-mighty-central-limit-theorem-b8152b94258">Central Limit Thm</a>
<a href="https://docs.google.com/presentation/d/1n-EQbPs3aH_wd_8v-E3lJiYA5m3Rwwhh/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">DetailedSlide_of_Part1_Probability (Optional)</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1Jr8ovwMCnIBT0Dyc3_VKMp9W_9hweYHO/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">5.Part1-Concise_BasicProbability</a>
<a href="https://docs.google.com/presentation/d/1yaRb50jX1ZbDEvhkuFWmflko4AKghZN0/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">5.Part2-SummaryStatistics</a>
</td>
<td>
HW2 Out (Jun 05)
</td>
</tr>
<tr>
<th scope="row">6</th>
<td>Jun 08</td>
<td> Data Exploration and Data Visualization
</td>
<td>
A
</td>
<td>
<!-- <a href="https://colab.research.google.com/drive/1B0hCLPsGWi2aepKHxtG-PohXZ-piHi39?usp=sharing">Colab Used </a>
<a href="https://drive.google.com/file/d/1lEJVdHjDDHZTfx_J7R-lfoLmAWjAlVbM/view?usp=sharing">Dataset</a> -->
</td>
<td>
<a href="https://docs.google.com/presentation/d/1xmkaapnfK5Xnfpnif0y5DtSmOTfcG_Yc/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec6.1DataExploration</a>
<a href="https://docs.google.com/presentation/d/1ex5pnPjbftQNigbOKMHChNemcVzVAXs-/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec6.2DataVizualization</a>
<td>
HW1 Due (Jun 08)
</td>
</tr>
<tr>
<th scope="row">7</th>
<td>Jun 09</td>
<td>Data Cleaning
</td>
<td>
S
</td>
<td>
<!-- <a href="https://colab.research.google.com/drive/10kS9JcQDsvwTMhX_P5Pj3fnsHQQ6Wrwf?usp=sharing">Class Google Colab</a> -->
</td>
<td>
<a href="https://docs.google.com/presentation/d/1grvlugDLphq3kkFcMwZKWzbNir1i8IOE/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec7DataCleaning_Part01</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">8</th>
<td>Jun 10</td>
<td> Data Cleaning cont.
</td>
<td>
S
</td>
<td>
<a href="https://www.shiksha.com/online-courses/articles/handling-missing-data-mean-median-mode/">HandlingMissingData</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1e-hAjRFJcJk59cNz3YWKDZT0nygMHGWB/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec08DataCleaning_Part02</a>
</td>
<td>
Project First Checkpoint Due (Jun 10)
</td>
</tr>
<tr>
<th scope="row">9</th>
<td>Jun 11</td>
<td>
Introduction to ML
</td>
<td>
A
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1MVu3H-Lfm6QoK4S3sGPubD_HAnYaR5x0/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec09IntroToML</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">10</th>
<td>Jun 12</td>
<td><strong>Discussion Session by TA Guanhong</strong>
</td>
<td>
S
</td>
<td>
</td>
<td>
<!-- A pre-recorded version is available in Discussion Folder in Panepto,Canvas. -->
<a href="https://docs.google.com/presentation/d/1yoZ42yZFglj1N6JXra7LYCOn0bK3tB3lG4OPC_PRv-U/edit?slide=id.g3eba0fbd985_1_0#slide=id.g3eba0fbd985_1_0">Discussion1_Slides</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">11</th>
<td>Jun 15 </td>
<td>Feature Engineering
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1i3qK4tIoeKauxlGHepU-2w7BAD8vDyI0/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec10FeatureEngineering</a>
</td>
<td>
HW3 Out (Jun 15)
</td>
</tr>
<tr>
<th scope="row">12</th>
<td>Jun 16</td>
<td>ML Evaluation
</td>
<td>
S
</td>
<td>
<a href="https://indhumathychelliah.com/2020/12/23/confusion-matrix%E2%80%8A-%E2%80%8Aclearly-explained/">Confusion Matrix</a> <br>
<a href="https://youtu.be/fSytzGwwBVw?si=khWbup2E19JGFvQJ">CrossValidationVideo</a>
<a href="https://www.youtube.com/watch?v=kituDjzXwfE">KFoldVideo</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1euRFzlP4saICSB3S_E1JfEFYf5GwpQMg/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec11ML_Evaluation</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">13</th>
<td>Jun 17</td>
<td>Decision Trees and Other Classifications
</td>
<td>
S (Decision Tree)
A (Other Classifications)
</td>
<td>
<!--<a href="https://victorzhou.com/blog/information-gain/?fbclid=IwAR0_tE6FkK7nGDXohbSEXxqFDKVotHhB0pJwob4OGbKfj-m8n-QOCDBI8M4">DecisionTree-Calculate-Entropy-InfoGain-Fardina</a> -->
<a href="https://victorzhou.com/blog/information-gain/?fbclid=IwAR0_tE6FkK7nGDXohbSEXxqFDKVotHhB0pJwob4OGbKfj-m8n-QOCDBI8M4">DecisionTree-Calculate-Entropy-InfoGain</a>
Reading materials are given at end of the Classification lecture slide
</td>
<td>
<a href="https://docs.google.com/presentation/d/1xcl6zTgqebsqUa2iaxISTKcv90zWESpr/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec12DecisionTree </a>
<a href="https://docs.google.com/presentation/d/1D7VyVHPklHRztYwmHDar2H64OPbMfOc4/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec13Classifications</a>
</td>
<td>
HW2 Due (Jun 17)
</td>
</tr>
<tr>
<th scope="row">15</th>
<td>Jun 18</td>
<td><strong>Discussion Session by TA Shengjie</strong>
</td>
<td>
S
</td>
<td>
</td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1p5jLwfkbBkhJvWM6w4dz9EznuTjbQD2p-kThWWZM93k/edit?usp=sharing">Discussion_Slides_Session2</a>
-->
</td>
<td>
HW 4 Out (Jun 18/19)
</td>
</tr>
<tr>
<th scope="row">14</th>
<td>Jun 19</td>
<td> <strong>Juneteenth Holiday</strong>
<td>
<!-- S -->
<td>
</td>
</td>
<td>
<strong>No Class</strong>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">16</th>
<td>Jun 22</td>
<td>Regression</strong>
</td>
<td>
S
</td>
<td>
<a href="https://drive.google.com/file/d/1wdRweYLoMjNF5kPgt7T0vV2xnqaBQm0N/view?usp=sharing">Simple Linear Regression</a>
<a href="https://www.kaggle.com/code/thaddeussegura/enough-to-be-dangerous-lasso-and-ridge-regression">Lasso&Ridge_Regression_Regularization</a>
<a href="https://docs.google.com/presentation/d/18vT5IuYAU86T5uNNPZEC1sJCS3T31MGO/edit">Detailed Slides from Previous Semester(Optional)</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1Z1MaPf5MYRoQ3EJ4gAxhUVK40O7T0RKh/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec14Regression</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">17</th>
<td>Jun 23</td>
<td> Unsupervised Learning - Dimensionality Reduction
</td>
<td>
A
</td>
<td>
<!--
<a href="https://docs.google.com/presentation/d/1DkqltXbHqYS6HDmm8HQfzfvNT2ni6ib3Lx_O-w3EmvA/edit?usp=sharing">PCA_overview_slides</a> -->
</td>
<td>
<a href="https://docs.google.com/presentation/d/1WIRH6I21utwvJEOFfH5kF-s4HJTxZEQ6/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec15-PCA-UnsupervisedLearning_DimReduction</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">18</th>
<td>Jun 24</td>
<td> Unsupervised Learning - Clustering
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1KaF_w2iWyAYHGFcseilWtbdZPzCApMpD/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec15UnsupervisedLearning_Clustering</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">19</th>
<td>Jun 25</td>
<td> Introduction to Neural Network
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1szGmp-yKQB7ZzKuDJVrSS6_gtF6a2V0hl0K0OGIPfBk/edit?usp=sharing">Lec16.1_NeuralNetwork </a> -
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">20</th>
<td>Jun 26</td>
<td><strong>Discussion Session by TA Shengjie</strong>
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1HAybK2bVEDSvXYHrnUbREtYeB6iGg3poE3pxHMN2sWc/edit?usp=sharing">Discussion3_Slides</a>
</td>
<td>
Project Second Checkpoint Due (Jun 26)
</td>
</tr>
<tr>
<th scope="row">21</th>
<td>Jun 29</td>
<td> Neural Network cont.
</td>
<td>
S
</td>
<td>
</td>
<td>
<!-- <a href="">Lec16NeuralNetwork </a> -->
<!--
<a href="https://docs.google.com/presentation/d/1KitZgYhdJnVo21-t6TPKNcjL4kewwVeb4CumjTk375I/edit?usp=sharing">Lec16.2_NN_Training</a> -->
</td>
<td>
HW3 Due (Jun 29)
</td>
</tr>
<tr>
<th scope="row">22</th>
<td>July 30</td>
<td> Image Processing with CNN
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1Mf51TlKpPzhg-9HnVmnso8YIJi03hV_zbTRP_eS0mao/edit?usp=sharing">Lec17ImageProcessing</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">23</th>
<td>July 01</td>
<td>Intro to Natural Language Processing (NLP)
</td>
<td>
A
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1DEM2UFGGp2ihdc281mhskdLn4QaTG4sU/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec18NLP</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">24</th>
<td>July 02</td>
<td>
Intro to Graph Theory
</td>
<td>
S (Part 1),
A (Part 2)
</td>
<td>
<a href="https://networkx.org/">NetworkX</a> <br>
<a href="https://graphql.org/learn/">Intro to GraphQL </a> <br>
These two materials are given for self-exploration if someone is interested.
<a href="https://drive.google.com/file/d/1UQsL6YxzbwcA_q6UDv_p0UislonKfbBt/view?usp=sharing">ClassNote_Girvan-NewmanAlgorithm</a>
<br>
<a href="https://cmsc320.github.io/2021fall/files/girvan-newman.pdf">Girvan & Newman. "Community structure in social and biological networks," PNAS-02.</a>
</td>
<td>
<a href="https://docs.google.com/presentation/d/15rUIHVwMVgKyIqDwyx2JkcXMrk_eZKtT/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec19_GraphTheory_P1</a>
<a href="https://docs.google.com/presentation/d/1OzFgImH9CknS6Q-BzhuYEy747lJ9a47v/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec19_GraphTheory_P2</a>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">25</th>
<td>July 03</td>
<td><strong>Independence Day Holiday</strong>
</td>
<td>
<strong>NO CLASS</strong>
</td>
<td>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr>
<th scope="row">26</th>
<td>July 06</td>
<td>Recommender System
</td>
<td>
S
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/11EXeXYAewSJlTaUXPvWTlh6gXjaR273B_iU2YSP57uM/edit?usp=sharing">Lec20RecommendationSystem_Lecture01</a>
</td>
<td>
HW4 Due (July 06)
</td>
</tr>
<tr>
<th scope="row">27</th>
<td>July 07</td>
<td>Recommender System and Data Ethics
</td>
<td>
S (Lec21)
A (Lec 22 - Self Reading for the quiz)
</td>
<td>
</td>
<td>
<a href="https://docs.google.com/presentation/d/1J6sb4cww37FTIHHNV4ZiG8ydGJsQ3kDX/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true" >Lec21RecommendationPart02</a>
<a href="https://docs.google.com/presentation/d/1QbpQ4Fa7XYYxTUvZfct0SYN5Rio8_Uvi/edit?usp=sharing&ouid=104928873451736790560&rtpof=true&sd=true">Lec22DataEthics(SELF STUDY FOR QUIZ)</a>
</td>
<td>